阅读说明：本技术 一种卷装长丝的毛丝缺陷检测方法 (Method for detecting broken filament defects of packaged filaments ) 是由 杨崇倡 肖凌云 冯培 张荣根 宋洪征 于 2018-08-24 设计创作，主要内容包括：本发明公开了一种卷装长丝的毛丝缺陷检测方法。该毛丝缺陷检测方法,包括：获取卷装长丝的待检测区域的多张采集图像；截取采集图像中与待检测区域内的受检位置对应的目标图像；确定目标图像中的连续长曲线和连续长曲线上的多根短线；将目标图像分为多张子图像,并确定各张子图像中的短线的线条几何参数和子图像的图像几何参数；根据线条几何参数和图像几何参数确定卷装长丝是否存在毛丝缺陷。本发明实施例的卷装长丝的毛丝缺陷检测方法,能够降低人工目测的错误率,提高检测效率,且能够节约生产成本。(The invention discloses a broken filament defect detection method for a package filament. The method for detecting the broken filament defects comprises the following steps: acquiring a plurality of collected images of a to-be-detected area of the coiled filament; intercepting a target image corresponding to a detected position in a region to be detected in the collected image; determining a continuous long curve in a target image and a plurality of short lines on the continuous long curve; dividing the target image into a plurality of sub-images, and determining line geometric parameters of short lines in each sub-image and image geometric parameters of the sub-images; and determining whether the packaged filament has broken filament defects according to the line geometric parameters and the image geometric parameters. The method for detecting the broken filament defects of the package filaments can reduce the error rate of manual visual inspection, improve the detection efficiency and save the production cost.)

1. A method for detecting a broken yarn defect of a package filament is characterized by comprising the following steps:

acquiring a plurality of collected images of the to-be-detected area of the coiled filament;

intercepting a target image corresponding to a detected position in the region to be detected in the collected image;

determining a continuous long curve in the target image and a plurality of short lines on the continuous long curve;

dividing the target image into a plurality of sub-images, and determining line geometric parameters of the short lines in each sub-image and image geometric parameters of the sub-images;

and determining whether the packaged filament has broken filament defects according to the line geometric parameters and the image geometric parameters.

2. The method of claim 1, wherein the inspection location comprises a first tapered surface of the packaged filament, and acquiring the plurality of captured images of the area of the packaged filament to be inspected comprises:

a plurality of collected images including the first tapered surface are acquired along the circumferential direction of the wound filament, respectively.

3. The method of detecting a yarn defect in a wound filament according to claim 2, wherein capturing the target image corresponding to the detected position in the area to be detected in the collected image comprises:

acquiring a copy image of the acquired image;

denoising the duplicate image, and acquiring a plurality of edges in the denoised duplicate image by using an image segmentation algorithm;

determining a plurality of edge lines of the detected position according to the plurality of edge lines, and acquiring coordinate values of all pixel points of the plurality of edge lines;

and intercepting the target image in the acquired image according to the coordinate values of all pixel points of the plurality of edge lines.

4. The method of detecting a fuzz defect in a packaged filament according to claim 3, wherein determining a plurality of edge lines of the inspected position according to the plurality of edge lines comprises:

determining a first edge line, a second edge line and a third edge line of the first conical surface according to the plurality of edge lines, and determining a first intersection point of the first edge line and the second edge line and a second intersection point of the first edge line and the third edge line;

determining a reference line according to the first intersection point and the second intersection point, and rotating the copied image to enable the reference line to be horizontally arranged;

and determining coordinate values of all pixel points of the rotated first intersection point, the rotated second intersection point and the rotated first edge line, and determining a plurality of edge lines of the detected position.

5. The method of detecting a yarn defect in a package of filaments according to claim 1, wherein determining a continuous long curve in the target image and a plurality of short lines on the continuous long curve comprises:

denoising the target image;

filtering the background pattern of the denoised target image to obtain an image to be analyzed;

and determining the continuous long curve and the short lines in the image to be analyzed by using a dilation processing method.

6. The method of claim 5, wherein filtering the denoised background pattern of the target image to obtain an image to be analyzed comprises:

determining a pseudo edge of the detected position in the denoised target image by utilizing an edge detection algorithm;

deleting the false edges in the target image;

and filtering the background of the target image with the false edge deleted by using a binarization method to obtain the image to be analyzed.

7. The method of detecting defects in the tail of a wound filament according to claim 5, wherein determining said continuous length curve and said plurality of stubs in said image to be analyzed by a dilation process comprises:

and filling a region smaller than a preset area between every two adjacent first patterns in the image to be analyzed by using an expansion processing method to obtain the continuous long curve and the short lines.

8. The method of claim 1, wherein the line geometry parameters of the short line include line length, line width, and the angle of the short line with respect to vertical, and the image geometry parameters of the sub-image include sub-image length.

9. The method of detecting tail yarn defects in a wound filament of claim 8 wherein determining whether said wound filament has a fuzz defect based on said line geometry and said image geometry comprises:

setting short lines with the line length meeting a first preset threshold, the line width meeting a second preset threshold and the included angle meeting a third preset threshold as marking objects;

determining a sum of the number of the marker objects in sub-images of the same region of the plurality of acquired images;

and determining whether the packaged filament has broken filament defects according to whether the first ratio of the sum of the number to the sub-image length meets a fourth preset threshold value.

10. The method of detecting a defect in a tail yarn of a wound filament according to claim 9, wherein determining whether the target image has a defect in a broken yarn further comprises:

and when the yarn defect of the packaged filament is determined, determining the yarn defect grade of the packaged filament according to the first ratio.

Technical Field

The invention relates to the technical field of surface detection of packaged filaments, in particular to a broken filament defect detection method of packaged filaments.

Background

The package filament is a package finished product with certain shape and capacity which is made by a winding mechanism in the production process of the filament. The defects of the wound filament are mainly divided into two parts of physical and chemical property defects of the filament and appearance defects of the wound filament. It has been found through practice that appearance defects in the wound filaments have a significant impact on the quality of the fabric, resulting in a reduction in fabric yield. Therefore, in the production process, the detection of appearance defects of the wound filaments is intensified.

The broken filament defect of the packaged filament is caused by unstable process and equipment, and the subsequent processing has influence on warping, weaving and dyeing performance, so that the factory has high requirement on the detection of the broken filament, and if the packaged filament has the broken filament, the upper side surface, the lower side surface and the cylindrical surface of the packaged filament can simultaneously have the broken filament.

Because the coiled filament has the characteristics of deformability, multiple curved surfaces and large detected surface, the appearance defect characteristics of the coiled filament are difficult to extract unified standards, the appearance detection of the coiled filament always influences the realization of the intelligent production of a long production flow, and the broken filament defect detection can only be carried out by a manual visual inspection method.

Although the detection of the broken filament defects by manual visual inspection can detect partial defects, the manual visual inspection method increases the labor cost of production, and does not have a uniform and strict quality standard, so that the accuracy of the detection result is difficult to ensure.

Disclosure of Invention

In order to solve the above problems, embodiments of the present invention provide a method for detecting a broken filament defect of a package filament, which can reduce an error rate of manual visual inspection, improve detection efficiency, and save production cost.

In order to achieve the above object, an embodiment of the present invention provides a method for detecting a broken filament defect of a package filament, including:

acquiring a plurality of collected images of a to-be-detected area of the coiled filament;

intercepting a target image corresponding to a detected position in a region to be detected in the collected image;

determining a continuous long curve in a target image and a plurality of short lines on the continuous long curve;

dividing the target image into a plurality of sub-images, and determining line geometric parameters of short lines in each sub-image and image geometric parameters of the sub-images;

and determining whether the packaged filament has broken filament defects according to the line geometric parameters and the image geometric parameters.

Further, the inspected position includes a first tapered surface of the wound filament, and acquiring a plurality of collected images of the area to be inspected of the wound filament includes:

a plurality of collected images including a first cone surface are respectively acquired along the circumferential direction of the wound filament.

Further, intercepting a target image corresponding to the detected position in the region to be detected in the collected image includes:

acquiring a copy image of the acquired image;

denoising the duplicate image, and acquiring a plurality of edges in the denoised duplicate image by using an image segmentation algorithm;

determining a plurality of edge lines of the detected position according to the plurality of edge lines, and acquiring coordinate values of all pixel points of the plurality of edge lines;

and intercepting the target image in the collected image according to the coordinate values of all the pixel points of the plurality of edge lines.

Further, determining a plurality of edge lines of the examined position according to the plurality of edge lines comprises:

determining a first edge line, a second edge line and a third edge line of the first conical surface according to the plurality of edge lines, and determining a first intersection point of the first edge line and the second edge line and a second intersection point of the first edge line and the third edge line;

determining a reference line according to the first intersection point and the second intersection point, and rotating the copied image to enable the reference line to be horizontally arranged;

and determining coordinate values of all pixel points of the rotated first intersection point, the rotated second intersection point and the rotated first edge line, and determining a plurality of edge lines of the detected position.

Further, determining the continuous long curve and the plurality of short lines on the continuous long curve in the target image includes:

denoising the target image;

filtering the background pattern of the denoised target image to obtain an image to be analyzed;

and determining a continuous long curve and a plurality of short lines in the image to be analyzed by using an expansion processing method.

Further, filtering the background pattern of the denoised target image to obtain an image to be analyzed, including:

determining a pseudo edge of a detected position in the denoised target image by using an edge detection algorithm;

deleting a false edge in the target image;

and filtering the background of the target image with the false edges deleted by using a binarization method to obtain an image to be analyzed.

Further, the determining the continuous long curve and the plurality of short lines in the image to be analyzed by using the dilation method comprises:

and filling a region smaller than a preset area between every two adjacent first patterns in the image to be analyzed by using an expansion processing method to obtain a continuous long curve and a plurality of short lines.

Further, the line geometric parameters of the short lines include line length, line width and an included angle between the short lines and the vertical direction, and the image geometric parameters of the sub-images include sub-image length.

Further, determining whether the packaged filament has a broken filament defect according to the line geometric parameters and the image geometric parameters comprises:

setting short lines with the line length meeting a first preset threshold, the line width meeting a second preset threshold and the included angle meeting a third preset threshold as marking objects;

determining a sum of the number of marker objects in sub-images of the same area of the plurality of acquired images;

and determining whether the packaged filament has broken filament defects according to whether the sum of the number and the first ratio of the lengths of the sub-images meet a fourth preset threshold value.

Further, after determining whether the target image has a broken filament defect, the method further includes:

and when the yarn defect of the wound filament is determined, determining the yarn defect grade of the wound filament according to the first ratio.

The method for detecting the broken filament defects of the packaged filaments can be applied to a detection device on a production line, image acquisition is carried out on the packaged filaments passing through each detection station, corresponding acquired images are obtained, then the images are processed, defect characteristics (namely geometric characteristic parameters of a plurality of short lines on a continuous long curve) for judging the broken filament defects are extracted, and whether the packaged filaments have the broken filament defects or not is determined according to the properties of the defect characteristics of different broken filament defects.

The method for detecting the broken filament defects of the packaged filament is suitable for detecting the broken filament defects of the packaged filament in the chemical fiber field, can quickly analyze the appearance defects of the packaged filament, reduces the errors of manual visual inspection, reduces the false detection rate, improves the detection efficiency and reduces the production cost through high-precision analysis and processing of images.

Drawings

FIG. 1 is a flow chart of a method of detecting a fuzz defect in a package filament according to one embodiment of the present invention;

FIG. 2 is a schematic structural view of a wound filament according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an acquired image according to an embodiment of the invention;

FIG. 4 is a flowchart illustrating a specific method of step S120 in FIG. 1;

FIG. 5 is a flowchart illustrating a specific method of step S123 in FIG. 4;

FIG. 6 is a schematic diagram of a target image according to an embodiment of the invention;

FIG. 7 is a flowchart illustrating a specific method of step S130 in FIG. 1;

FIG. 8 is a flowchart of a detailed method of one embodiment of step S150 in FIG. 1;

fig. 9 is a flowchart illustrating another embodiment of a specific method in step S150 in fig. 1.

Detailed Description

The structure, operation principle, and the like of the embodiments of the present invention will be further described with reference to the accompanying drawings.

As shown in fig. 1, a method for detecting a broken filament defect of a package filament according to an embodiment of the present invention includes:

s110, acquiring a plurality of collected images of a to-be-detected area of the coiled filament;

s120, intercepting a target image corresponding to the detected position in the region to be detected in the collected image;

s130, determining a continuous long curve in the target image and a plurality of short lines on the continuous long curve;

s140, dividing the target image into a plurality of sub-images, and determining line geometric parameters of short lines in each sub-image and image geometric parameters of the sub-images;

s150, determining whether the packaged filament has broken filament defects according to the line geometric parameters and the image geometric parameters.

In an embodiment of the invention, as shown in fig. 2, the inspected location comprises a first cone 1 of the wound filament, and acquiring a plurality of collected images of the area to be inspected of the wound filament comprises:

a plurality of collected images including the first tapered surface 1 are acquired along the circumferential direction of the wound filament.

In the embodiment of the present invention, since the yarn defect occurs simultaneously on the first tapered surface 1 at the upper end of the wound filament, the second tapered surface 2 at the lower end of the wound filament, and the cylindrical surface 3 of the wound filament, any one surface can be selected for detection. Since the defect of the broken filament can be more easily determined according to the first tapered surface 1, the first tapered surface 1 can be selected as the inspected position in the embodiment of the present invention, and the area of the wound filament including the first tapered surface 1 is used as the area to be inspected.

Therefore, an area-array camera may be disposed at a position corresponding to the region to be detected, the area-array camera may be disposed perpendicular to the first tapered surface 1, and may be capable of acquiring a captured image as shown in fig. 3. The area-array camera can reduce the shooting times and save time. And according to the characteristics of fixed position and limited shooting angle of the area-array camera, the coiled filament can be rotated, and the collected image of the area to be detected is collected while the coiled filament rotates for one circle, so that the image collection of the whole circle of the coiled filament is completed. In one embodiment, the photographing time may be 2.2s, and 30 acquired images of the region to be detected are acquired at equal time intervals within the photographing time.

Because the defect characteristic of the broken filament on the surface of the object to be detected can be highlighted by using low-angle illumination, in the embodiment of the invention, the area to be detected is illuminated by adopting the low-angle illumination light source, and the light source light ray area-array cameras have the same acquisition direction.

In the embodiment of the present invention, as shown in fig. 4, intercepting a target image corresponding to a detected position in a region to be detected in a captured image includes:

s121, acquiring a copy image of the acquired image;

s122, denoising the duplicate image, and acquiring a plurality of edges in the denoised duplicate image by using an image segmentation algorithm;

s123, determining a plurality of edge lines of the detected position according to the plurality of edge lines, and acquiring coordinate values of all pixel points of the plurality of edge lines;

and S124, intercepting the target image from the acquired image according to the coordinate values of all the pixel points of the plurality of edge lines.

As shown in fig. 5, the determining the edge lines of the detected position according to the edge lines in step S123 may include:

s210, determining a first edge line 13, a second edge line 11 and a third edge line 13 of the first conical surface 1 according to a plurality of edge lines, and determining a first intersection point 14 of the first edge line 13 and the second edge line 11 and a second intersection point 15 of the first edge line 13 and the third edge line 13;

s220, determining a reference line 16 according to the first intersection point 14 and the second intersection point 15, and rotating the copied image to horizontally arrange the reference line 16;

and S230, determining coordinate values of all pixel points of the rotated first intersection point 14, the rotated second intersection point 15 and the rotated first edge line 13, and determining a plurality of edge lines of the detected position.

In the embodiment of the invention, the interference of a non-processing object in the image can be eliminated by intercepting the target image in the acquired image. By adopting the steps, the situation that the detected position has deviation or the detected position ranges are different can be processed, and the image capturing precision is improved.

In the embodiment of the invention, the acquired image can be copied firstly, the copied image can be obtained, and the edge line can be obtained by processing the copied image, so that the influence on the subsequent processing of the original acquired image caused by processing the original acquired image can be prevented.

Then, denoising processing may be performed on the copied image through a filtering method, and in this embodiment, the filtering method may preferably be gaussian filtering. The denoised copy image can obtain a plurality of edge lines through an image segmentation algorithm. The image segmentation algorithm can be an edge detection algorithm or a binarization method, and the sideline can be a sudden change position of gray level brightness change of the edge of the first conical surface 1 of the coiled filament.

Next, an edge closest to the top of the image in the copied image may be selected as a first edge 13, an edge closest to the left of the image in the copied image may be selected as a second edge 11, an edge closest to the right of the image in the copied image may be selected as a third edge 13, a first intersection 14 of the first edge 13 and the second edge 11, and a second intersection 15 of the first edge 13 and the third edge 13 are determined, and at the same time, coordinates (x1, y1) of the first intersection 14 and coordinates (x2, y2) of the second intersection 15 are obtained.

Again, reference line 16 is obtained by connecting first intersection 14 and second intersection 15. An angle phi between reference line 16 and a horizontal reference line 16 is measured, and the copied image is rotated about first intersection 14 as a rotation axis based on the angle phi between reference line 16 and reference line 16, so that reference line 16 is horizontally arranged. At this time, the coordinates of the second intersection point 15 become (x3, y 1).

Then, an average y coordinate value of all the pixels of the rotated first edge 13 is obtained, an edge line coordinate y3 at the top end of the first edge 13 is obtained by adding the average y coordinate value to a preset coordinate difference value, and an edge line coordinate y4 at the bottom end of the first edge 13 is obtained by subtracting the preset coordinate difference value from the average y coordinate value. Therefore, the left edge line x is x1, the right edge line x is x3, the upper edge line y is y3, and the lower edge line y is the coordinate values of all the pixels in the copied image in y 4.

Finally, the coordinate values of all the pixel points of the edge line in the copied image can be brought into the acquired image, the same pixel point coordinate values of the acquired image are extracted, a plurality of segmentation edge lines of the acquired image can be obtained by using the extracted coordinate values, wherein the segmentation edge lines comprise a left edge line, a right edge line, an upper edge line and a lower edge line, and then the image in the segmentation edge lines is captured to obtain the target image shown in fig. 6.

In an embodiment of the present invention, as shown in fig. 7, determining the continuous long curve and the plurality of short lines on the continuous long curve in the target image includes:

s131, denoising the target image;

s132, filtering the background pattern of the denoised target image to obtain an image to be analyzed;

and S133, determining a continuous long curve and a plurality of short lines in the image to be analyzed by using an expansion processing method.

In the embodiment of the present invention, the noise point in the target image may be analyzed first, and then the target image may be filtered according to the existing noise point. Since the noise points of the target image are concentrated in the high-frequency region, the filtering process may employ a Butterworth low-pass filter. The Butterworth low-pass filter has a stable amplitude characteristic, and can obtain an image for eliminating isolated noise points, so that the problem that the imaging quality of the image is deteriorated due to various interference factors in the process of acquiring an original signal, transmitting the original signal to signal conversion and signal transmission of the area-array camera is solved.

In the embodiment of the present invention, filtering the background pattern of the denoised target image to obtain the image to be analyzed includes:

determining a pseudo edge of a detected position in the denoised target image by using an edge detection algorithm;

deleting a false edge in the target image;

and filtering the background of the target image with the false edges deleted by using a binarization method to obtain an image to be analyzed.

Specifically, the edge detection can be performed by using a Prewitt operator + logarithmic form calculation or a Roberts operator + logarithmic form, so as to obtain a plurality of edges. And then, according to the gray difference of upper, lower, left and right adjacent points of the pixel point, an extreme value detection edge is achieved at the edge, part of false edges are removed, the image enhancement effect is obvious, the original details of the target image are not lost, and the image enhancement effect is optimal.

In the embodiment of the invention, the target image after the edge detection processing can be subjected to binarization processing according to a preset optimal threshold value, so that an image with only red and black colors is obtained, and background interference is removed. Wherein, the optimal threshold value can be set to 90-95.

In the embodiment of the present invention, determining the continuous long curve and the plurality of short lines in the image to be analyzed by using the dilation method includes:

and filling a region smaller than a preset area between every two adjacent first patterns in the image to be analyzed by using an expansion processing method to obtain a continuous long curve and a plurality of short lines.

In the embodiment of the invention, the expansion processing method is utilized to connect the first pattern to obtain the continuous long curve and the plurality of short lines, so that unnecessary interference on the image to be analyzed after the background pattern is filtered can be avoided. Specifically, the image to be analyzed may be divided into a plurality of blocks, and then the red color image may be connected according to a red image of a predetermined area to obtain a continuous long curve and a plurality of short lines. Because the red image with the preset area can effectively fill the area which is smaller than the preset area between every two adjacent first patterns, the boundary of the line can be smoothed under the condition that the line is obviously not changed, and the subsequent extraction of the geometric parameters of the line is convenient.

In the embodiment of the invention, the line geometric parameters of the short line comprise the line length, the line width and the included angle between the short line and the vertical direction, and the image geometric parameters of the subimage comprise the subimage length.

In one embodiment, each target image may be divided into 3 sub-images, a first sub-image, a second sub-image, and a third sub-image.

In the embodiment of the present invention, as shown in fig. 8, the determining whether the yarn defect exists in the wound filament according to the line geometry parameter and the image geometry parameter includes:

s151, setting short lines with the line length meeting a first preset threshold, the line width meeting a second preset threshold and the included angle meeting a third preset threshold as marking objects;

s152, determining the sum of the number of the marking objects in the sub-images of the same area of the multiple acquired images;

s153, determining whether the packaged filament has broken filament defects according to whether the sum of the number and the first ratio of the sub-image lengths meet a fourth preset threshold value.

In the embodiment of the invention, the first preset threshold value is more than or equal to 2mm, the second preset threshold value is more than or equal to 0.04mm, and the third preset threshold value is-20 degrees. If all the line geometric parameters of the short line do not meet the threshold value, namely the line length is less than 2mm or the line width is less than 0.04mm or the included angle is between 0 and 20 degrees, the short line is not considered as the marking object.

Taking the example that the number of the acquired images is 30 and the target image corresponding to each acquired image is divided into 3 sub-images, when the mark object in each sub-image is determined, the number of the mark objects in each sub-image is obtained, and then the sum of the number of the mark objects in 30 first sub-images, 30 second sub-images and 30 third sub-images is obtained respectively.

Then, taking the sum of the number of the marking objects in the 30 first sub-images as an example, the first ratio of the sum of the number to the length of the first sub-image may be compared with a preset fourth preset threshold value to determine whether the yarn defect exists in the wound filament.

Taking the AAA grade type of packaged filament as an example, the fourth preset threshold may be set to be less than or equal to 1.8, and when the first ratio meets the fourth preset threshold, it is determined that the defect of the broken filament does not exist, and when the first ratio does not meet the fourth preset threshold, it is determined that the defect of the broken filament exists.

In another embodiment of the present invention, as shown in fig. 9, after determining whether the target image has a broken filament defect, the method further includes:

and S154, determining the broken filament defect grade of the wound filament according to the first ratio after determining that the wound filament has broken filament defects.

In one embodiment of the invention, by taking the example that the number of the collected images is 30, the target image corresponding to each collected image is divided into 3 subimages, and the broken filament defect of the packaged filament of the AAA grade variety is detected, when the first ratio is less than or equal to 1.8, the broken filament defect does not exist; when the first ratio is in the range of 1.8-2.4, the yarn is a degraded broken yarn defect of A grade; when the first ratio is more than or equal to 2.4, the yarn belongs to the graded degraded broken yarn defect.

If 1 or more than 1 of the first ratios of the 30 first sub-images, the 30 second sub-images and the 30 third sub-images meets the B-level degradation, judging that the first conical surface 1 of the wound filament is B-level degradation; if 0 meets grade B degradation and 1 or more meets grade A degradation, judging the first conical surface 1 of the coiled filament to be grade A degradation; if 0 meets grade B degradation and 0 meets grade a degradation, the first cone 1 of the wound filament is judged to be AAA grade, i.e., no fuzz defect.

In one embodiment of the invention, the number of the collected images is 30, the target image corresponding to each collected image is divided into 3 subimages, and the broken filament defect of the packaged filament of the AAA grade variety is detected as an example, when the first ratio is less than or equal to 2.1, the broken filament defect does not exist; when the first ratio is in the range of 2.1-2.7, the yarn is a degraded broken yarn defect of A grade; when the first ratio is more than or equal to 2.7, the yarn belongs to the graded degraded broken yarn defect.

If 1 or more than 1 of the first ratios of the 30 first sub-images, the 30 second sub-images and the 30 third sub-images meets the B-level degradation, judging that the first conical surface 1 of the wound filament is B-level degradation; if 0 meets grade B degradation and 1 or more meets grade A degradation, judging the first conical surface 1 of the coiled filament to be grade A degradation; if 0 meets grade B degradation and 0 meets grade a degradation, the first cone 1 of the wound filament is judged to be grade AA, i.e., no fuzz defect. According to the method for detecting the broken filament defect of the packaged filament, disclosed by the embodiment of the invention, the broken filament defect rate can be correctly detected to reach 98.2%, the efficiency is 4 seconds per ingot, the method is superior to a manual visual inspection method, the online automatic detection of the appearance quality of the packaged filament is realized, and the manual workload is reduced by 95%.

In conclusion, the method for detecting the broken filament defects of the packaged filaments can be widely applied to online detection of the broken filament defects of the packaged filaments in the chemical fiber field, and appearance defects can be easily analyzed. And the broken filament defect is detected by a method for extracting and judging the broken filament defect characteristics, the defect grade can be automatically identified, the detection precision can be improved, and the error caused by manual visual inspection can be reduced. Meanwhile, in the image processing process, interference can be eliminated, the defect of broken filaments can be rapidly judged, and the false detection rate is reduced.

The foregoing is merely illustrative of the present invention, and it will be appreciated by those skilled in the art that various modifications may be made without departing from the principles of the invention, and the scope of the invention is to be determined accordingly.