阅读说明：本技术 基于背景消除和改进连通域算法的fod自动识别方法及系统 (FOD automatic identification method and system based on background elimination and improved connected domain algorithm ) 是由 陈晓林 刘峰 姜官男 许学凡 谭越 徐晓浩 于 2020-12-30 设计创作，主要内容包括：本发明提供了一种基于背景消除和改进连通域算法的FOD自动识别方法及系统,包括：步骤M1：获取雷达数据并通过雷达数据判断是否存在背景数据；当同时存在正向和反向的背景数据时,则使用背景对消FOD目标检测算法进行FOD目标检测,输出FOD目标检测结果图像及目标坐标文档；当不是同时存在正向和反向的背景数据时,则使用FOD目标检测算法进行FOD目标检测,并判断根据FOD目标检测算法得到的FOD报警数据变量是否为0,当FOD报警数据变量为0时,则将当前的雷达数据存为新的背景数据,重复执行步骤M1；当FOD报警数据变量不为0时,则输出FOD目标检测结果图像及目标坐标文档；本发明中处理过程的所有参数,均可以根据机场实际场景进行分段处理,算法适应性高。(The invention provides an FOD automatic identification method and system based on background elimination and improved connected domain algorithm, comprising the following steps: step M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, the FOD target detection algorithm is used for detecting the FOD target, whether the FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the step M1 is repeatedly executed; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document; all parameters of the processing process in the invention can be processed in a segmented manner according to the actual scene of the airport, and the algorithm adaptability is high.)

1. An FOD automatic identification method based on background elimination and improved connected domain algorithm is characterized by comprising the following steps:

step M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, the FOD target detection algorithm is used for detecting the FOD target, whether the FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the step M1 is repeatedly executed; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

2. The FOD automatic identification method based on background elimination and improved connected domain algorithm according to claim 1, wherein the FOD target detection is performed by using the background elimination FOD target detection algorithm, and outputting the FOD target detection result image and the target coordinate document comprises: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

3. The FOD automatic identification method based on background elimination and improved connected domain algorithm according to claim 2, characterized in that said white pixel labeling in all connected regions based on preset blob detection operator parameters comprises: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

4. The FOD automatic identification method based on background elimination and improved connected component domain algorithm of claim 3, characterized in that the outline of the connected component region and the value of the circumscribed rectangle comprise: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

5. The FOD automatic identification method based on background elimination and improved connected domain algorithm according to claim 2, characterized in that the improved connected domain algorithm comprises:

step M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

step M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

step M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

step M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

6. An FOD automatic identification system based on background elimination and improved connected domain algorithm, which is characterized by comprising:

module M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, an FOD target detection algorithm is used for detecting an FOD target, whether an FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the execution of the module M1 is repeatedly triggered; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

7. The FOD automatic identification system based on background elimination and improved connected domain algorithm according to claim 6, wherein the FOD target detection is performed by using the background elimination FOD target detection algorithm, and outputting the FOD target detection result image and the target coordinate document comprises: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

8. The FOD automatic identification system based on background elimination and improved connected domain algorithm according to claim 7, characterized in that said white pixel labeling in all connected regions based on preset blob detection operator parameters comprises: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

9. The FOD automatic identification system based on background elimination and improved connected component domain algorithm of claim 8, characterized in that the outline of the connected component region and the value of the circumscribed rectangle comprise: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

10. The FOD automatic identification system based on background elimination and improved connected domain algorithm of claim 7, characterized in that the improved connected domain algorithm comprises:

module M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

module M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

module M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

module M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

Technical Field

The invention relates to the field of FOD radar image target automatic identification algorithms, in particular to an FOD target automatic identification method and an FOD target automatic identification system based on background elimination and an improved connected domain algorithm, and more particularly relates to an algorithm which is used for separating a target and a background based on a background elimination algorithm, effectively reducing false alarms and positioning and counting radar detection targets.

Background

Airport pavement foreign objects, also known as "FOD," generally refer to something foreign, debris or objects on the airport runway that may damage aircraft, such as metal parts, debris, pavement materials, flying objects, and the like. Runway foreign objects have a non-negligible impact on flight safety for safe operation at airports. Because the airport aircraft uses the runway to take off and land frequently, the runway foreign object detection will influence the normal take off and land of many flights every time, and more importantly, foreign objects are easy to miss or fall on the detection vehicle by means of manual detection, and the detection of the foreign objects on the runway surface has serious defects.

The radar system has high false alarm rate, and the radar false alarm rate in the FOD detection of the airport is an important index of the FOD detection system and is also an object which influences the FOD system users to pay most attention to. The clutter background of the airport runway where the FOD target is located is quite complex and relatively strong, the clutter has a great influence on the target, and the radar constant false alarm technology has been applied to FOD detection of radar echo, but the false alarm still influences the user experience to a great extent, especially the detection of tiny FOD. Therefore, the detection of targets in clutter background and the reduction of false alarm rate of radar signal detection are one of the important tasks of the airport runway Foreign Object (FOD) alarm system.

Traditional FOD target detection algorithm can report to the police at positions such as runway center light, sidelight, produce unnecessary interference for the user, can not effectively report to the police to the FOD target that drops on center light or sidelight simultaneously, brings the hidden danger for airport safe operation.

When the two groups of data are subjected to background cancellation, if the angles of the two groups of data are not aligned, a false alarm can be generated after the cancellation, and the false alarm generated due to the misalignment of the angles can be effectively reduced by combining a connected domain algorithm.

In view of the above-mentioned need of the FOD system, the technical problems to be solved by the present invention are as follows:

1) aiming at the spatial distribution characteristics of target points, a background cancellation method is utilized to effectively separate static background and dynamic targets, so that FOD targets on a runway center light and a side light can be effectively detected;

2) by adopting an improved connected domain algorithm based on an image connected domain, false alarms caused by misalignment of radar angle data during background cancellation are effectively reduced;

3) the area of each target area is extracted, the FOD target can be automatically identified, and the number of the FOD targets can be counted.

Patent document CN109765557A (application No. 201811647433.0) discloses a method, system and medium for FOD target adaptive fast classification and identification based on distribution characteristics, which includes: a target dividing step: according to the centralization of the first target area in the spatial distribution, the distance between target points is used as a target related characteristic quantity to realize the quantification of the similarity between the target points, the target points with the similarity higher than a similar prior neighborhood radius threshold are marked, then the target points with the marking position sum higher than the neighborhood size threshold are regarded as a first target area set, and the other targets are classified as a common distribution FOD target set.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide an FOD automatic identification method and system based on background elimination and an improved connected domain algorithm.

The FOD automatic identification method based on background elimination and improved connected domain algorithm provided by the invention comprises the following steps:

step M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, the FOD target detection algorithm is used for detecting the FOD target, whether the FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the step M1 is repeatedly executed; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

Preferably, the performing the FOD target detection by using the background cancellation FOD target detection algorithm, and outputting the FOD target detection result image and the target coordinate document includes: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

Preferably, the white pixel labeling in all connected regions based on the preset blob detection operator parameters includes: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

Preferably, the values of the outline and the circumscribed rectangle of the connected region include: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

Preferably, the improved connected component algorithm comprises:

step M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

step M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

step M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

step M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

The FOD automatic identification system based on background elimination and improved connected domain algorithm provided by the invention comprises:

module M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, an FOD target detection algorithm is used for detecting an FOD target, whether an FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the execution of the module M1 is repeatedly triggered; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

Preferably, the performing the FOD target detection by using the background cancellation FOD target detection algorithm, and outputting the FOD target detection result image and the target coordinate document includes: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

Preferably, the white pixel labeling in all connected regions based on the preset blob detection operator parameters includes: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

Preferably, the values of the outline and the circumscribed rectangle of the connected region include: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

Preferably, the improved connected component algorithm comprises:

module M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

module M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

module M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

module M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

Compared with the prior art, the invention has the following beneficial effects:

1. the FOD target detection method solves the problem that FOD targets on the center light and the side lights of the runway cannot be detected in the FOD target detection process, effectively classifies the targets and the background through the background cancellation algorithm, and improves the identification accuracy of the FOD system;

2. the connected domain algorithm can reduce false alarms generated by the background cancellation algorithm due to angle misalignment;

3. the FOD target identification processing process adopts a step-by-step rule, so that an invalid processing process is avoided, the algorithm processing speed is increased, and the requirement of a system on real-time performance is met;

4. all parameters of the processing process in the invention can be processed in a segmented manner according to the actual scene of the airport, and the algorithm adaptability is high.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:

FIG. 1 is a flow chart of a background cancellation algorithm;

FIG. 2 is a schematic illustration of a region of a connected domain;

FIG. 3 shows airport data acquisition results.

Detailed Description

The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.

Example 1

The FOD automatic identification method based on background elimination and improved connected domain algorithm provided by the invention comprises the following steps:

step M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, the FOD target detection algorithm is used for detecting the FOD target, whether the FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the step M1 is repeatedly executed; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

Specifically, the using the background cancellation FOD target detection algorithm to perform FOD target detection, and outputting an FOD target detection result image and a target coordinate document includes: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

Specifically, the white pixel labeling in all connected regions based on the preset blob detection operator parameters includes: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

Specifically, the outline of the connected region and the values of the circumscribed rectangle include: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

Specifically, the improved connected domain algorithm comprises:

step M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

step M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

step M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

step M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

The FOD automatic identification system based on background elimination and improved connected domain algorithm provided by the invention comprises:

module M1: acquiring radar data and judging whether background data exist or not through the radar data; when forward and reverse background data exist at the same time, FOD target detection is carried out by using a background cancellation FOD target detection algorithm, and an FOD target detection result image and a target coordinate document are output; when the forward background data and the reverse background data do not exist simultaneously, an FOD target detection algorithm is used for detecting an FOD target, whether an FOD alarm data variable obtained according to the FOD target detection algorithm is 0 or not is judged, when the FOD alarm data variable is 0, the current radar data is stored as new background data, and the execution of the module M1 is repeatedly triggered; when the FOD alarm data variable is not 0, outputting an FOD target detection result image and a target coordinate document;

the FOD target detection algorithm is used for carrying out FOD target detection based on Blob detection operator parameters in a preset OpenCV;

the FOD target detection algorithm for background cancellation carries out FOD target detection through the background cancellation algorithm and Blob detection operator parameters in a preset OpenCV.

Specifically, the using the background cancellation FOD target detection algorithm to perform FOD target detection, and outputting an FOD target detection result image and a target coordinate document includes: separating the target and the background of the acquired radar data by using a background cancellation algorithm to obtain target image data; extracting all connected regions in the image from the obtained target image data by using an improved connected region algorithm based on the image connected regions, and carrying out white pixel marking on all the connected regions based on preset spot detection operator parameters to obtain the FOD target number;

the improved connected domain algorithm finds out and marks all target connected regions existing in the target image by scanning the image twice.

Specifically, the white pixel labeling in all connected regions based on the preset blob detection operator parameters includes: and (3) using preset spot detection operator parameters, and combining the outline of the connected region and the value of the circumscribed rectangle to mark the white pixel.

Specifically, the outline of the connected region and the values of the circumscribed rectangle include: and the communication area acquires the outline of the block by utilizing a findContours function in the OpenCV, and acquires the minimum external rectangle of the communication area by utilizing a boundingRef function and matching the findContours function.

Specifically, the improved connected domain algorithm comprises:

module M2.1: setting the point of the pixel gray value higher than the threshold value as 1, and setting the point lower than the threshold value as 0 to form an n x m array;

module M2.2: carrying out first column direction scanning on the n x m array, marking a current column as 1 when the current column has pixel points with the value of 1, and recording the number of 1 appearing in the current column; when the current column does not have a pixel point with the value of 1, marking the current column as 0 to obtain a column direction one-dimensional array consisting of 0 and 1 and the number of 1 in the column marked as 1;

module M2.3: carrying out second-pass transverse scanning on the known columns with 1, marking the current row as 1 when the current row has pixel points with the value of 1, and recording the number of 1 appearing in the current row; when the current row does not have a pixel point with the value of 1, marking the current row as 0 to obtain a transverse one-dimensional array consisting of 0 and 1 and the number of 1 in the row marked as 1;

module M2.4: and determining the position of 1 according to the column direction one-dimensional array, the number of 1 appearing in the column marked as 1, the number of 1 appearing in the transverse one-dimensional array and the row marked as 1, and marking as the area of the connected domain.

Example 2

Example 2 is a modification of example 1

The method comprises the following steps: as shown in fig. 1 to 3:

step 1: the target and the background are separated by using a background cancellation algorithm, the intensity of ground clutter background signals can be effectively reduced, the signal-to-clutter ratio of target detection is improved, and FOD targets on the runway center light and the side lights are effectively detected. The principle of the background cancellation algorithm is that information which is fixed or relatively stable within a period of time is used as a background, when an FOD target falls on a runway, data of the FOD target is subtracted from the background data, and the target can be detected.

The implementation process of the background cancellation algorithm comprises the steps of collecting clean road surface data and storing the data as background data, storing the data of the radar from positive direction to negative direction as positive background data, and storing the data of the radar from negative direction to negative direction as negative background data. And when background cancellation is carried out in the later period, carrying out cancellation operation by using corresponding background data.

The FOD detection system is used for carrying out real-time temperature and humidity detection and weather change conditions on the airport, learning the background of the airport at different moments and reducing the interference of clutter on useful signals. And achieves the function of dynamically and intelligently replacing background data.

Step 2: by using an improved connected domain algorithm based on an image connected domain, each individual connected domain forms an identified block by marking white pixels (targets) in a binary image, and then geometric parameters such as outlines, circumscribed rectangles and the like of the blocks can be obtained. The false alarm caused by the misalignment of the angles during background cancellation is effectively avoided, and the false alarms of densely distributed common targets such as cables and bird repelling nets can be reduced.

And step 3: by connected component analysis (here, connected component analysis of binary images), the number of markers and thus the number of FOD targets can be obtained.

By scanning the image twice using Two-Pass, all connected regions present in the image can be found and marked. Setting a point with a pixel gray value higher than a threshold value as 1 and a point with a pixel gray value lower than the threshold value as 0 during first scanning, performing transverse scanning, recording the row coordinates of the pixel points with the value of 1, recording the current row as 1 when the pixel points with the value of 1 exist, and recording the current row as 0 when the pixel points with the value of 1 do not exist, thereby obtaining a row-direction one-dimensional array consisting of 0 and 1; the second scanning is to perform horizontal scanning on the column with 1 known in step M2.1, and in the same manner, record the position of the pixel with 1 to obtain a horizontal one-dimensional array. And recording coordinate values of the starting point of the area with 1 at the left side and the ending point of the area with 1 at the right side, namely the marked area of the connected domain. And the information of the whole block is not required to be recorded, so that a large amount of storage space can be saved, and the operation efficiency of the algorithm is prompted.

The simple steps of the Two-Pass algorithm are given below: as shown in figure 2 of the drawings, in which,

(1) first scanning:

accessing the current pixel B (x, y), recording as 1 if B (x, y) is greater than the threshold, otherwise recording as 0:

performing horizontal pixel-by-pixel scanning, recording the coordinates of the pixel points with the value of 1, recording the coordinates as 1 when the pixel points with the value of 1 exist in the current column, and recording the coordinates as 0 when the pixel points with the value of 1 do not exist in the current column, so as to obtain a column-direction one-dimensional array consisting of 0 and 1;

(2) and (3) second scanning:

and transversely scanning the column with 1 in the first scanning, and recording the positions of the pixel points with the 1 existence value in the same way to obtain a group of transverse one-dimensional arrays. As shown by the arrows in fig. 2, coordinate values of the start point of the area with 1 on the left side and the end point of the area with 1 adjacent on the right side are recorded, which are the areas of the marked connected domains.

After the scanning is completed, all connected regions in the image are extracted.

Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.

The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.