Aluminum foil surface defect detection system based on visual identification

Abstract

The invention discloses an aluminum foil surface defect detection system based on visual identification, and relates to the technical field of aluminum foil detection, and the system comprises a data acquisition module which is used for obtaining image data and environment data in a detection area; the data processing module is used for processing the obtained image data to obtain a region fusion layer; the image layer optimization module is used for carrying out image layer boundary optimization on the obtained region fusion image layer and splitting the region fusion image layer after boundary optimization to obtain an aluminum foil feature image layer; the defect identification module is used for carrying out surface defect analysis on the aluminum foil characteristic pattern layer and outputting a surface defect analysis result; according to the method, environment data including various standards are set, dynamic matching and layer fusion are performed during detection, and a self-adaptive image analysis reference is constructed, so that imaging quality fluctuation caused by environment illumination change is reduced, and the detection accuracy and reliability in a changeable industrial field are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of aluminum foil inspection technology, specifically to a visual recognition-based aluminum foil surface defect detection system. Background Technology

[0002] Aluminum foil, as an important industrial material, is widely used in packaging, electronics, power, and aerospace fields due to its excellent barrier properties, conductivity, and ductility. Its surface quality is a key indicator determining product performance and reliability. However, during the high-speed rolling or slitting process of aluminum foil, due to factors such as process fluctuations, environmental interference, and the material itself, the surface is prone to various microscopic and macroscopic defects such as pinholes, scratches, oil stains, bright spots, dark spots, and wrinkles. In existing technologies, the high reflectivity of aluminum foil surfaces makes image quality highly susceptible to environmental influences. Therefore, a visual recognition-based aluminum foil surface defect detection system is provided. Summary of the Invention

[0003] The purpose of this invention is to provide a visual recognition-based aluminum foil surface defect detection system.

[0004] The objective of this invention can be achieved through the following technical solution: a visual recognition-based aluminum foil surface defect detection system, including a monitoring center, and further including components that communicate with and / or are electrically connected to the monitoring center. The data acquisition module is used to acquire image data and environmental data within the detection area; The data processing module is used to process the acquired image data to obtain a region fusion layer; The layer optimization module is used to optimize the layer boundaries of the obtained region blending layer and split the region blending layer after boundary optimization to obtain the aluminum foil feature layer. The defect identification module is used to perform surface defect analysis on the feature layer of aluminum foil and output the surface defect analysis results.

[0005] Furthermore, the process by which the data acquisition module acquires image data and environmental data within the detection area includes: The data acquisition module includes a visual acquisition unit and an environmental acquisition unit; Set up a detection area, and deploy visual acquisition units at designated locations within the detection area. The shooting range of the deployed visual acquisition units completely covers the detection area. The deployed visual acquisition units acquire image data within the detection area in real time. Each corner of the detection area is equipped with a fixed marker point, and a corresponding environmental acquisition unit is deployed at the marker point. The environmental data of the corresponding location is acquired in real time through the deployed environmental acquisition unit. The environmental data includes light intensity and light angle.

[0006] Furthermore, before formally conducting aluminum foil defect detection, different standard environmental data are set for the detection area. Under different standard environmental data, image data is collected from the detection area where no aluminum foil is placed through the vision acquisition unit. Image data collected under different standard environmental data are recorded as the reference image layer of the corresponding standard environmental data, and a reference center and several reference points distributed in a circle with the reference center as the center are set in the reference image layer. Then, place the qualified aluminum foil in the detection area, and collect image data of the detection area through the vision acquisition unit; Image data collected under different standard environmental data are recorded as the corresponding standard environmental data reference image layer. A reference center and several reference points are set in the reference image layer, and the reference center and several reference points are all within the coverage area of ​​the aluminum foil.

[0007] Furthermore, the data processing module processes the acquired image data to obtain the region fusion layer, including the following steps: The obtained environmental data is matched with standard environmental data, and the baseline image layer and control image layer corresponding to the standard environmental data that is closest to the current environmental data are called. Construct a planar coordinate system and map the reference image layer and the comparison image layer to the planar coordinate system in sequence; Align the reference center of the reference image layer with the reference center of the control image layer, and each reference point corresponds to a control point to obtain a reference fusion layer. Record the positions of the corresponding reference center and control center as fusion center points, and the positions of the reference points and control points as fusion reference points. The obtained image data is used as the layer to be identified and mapped into a planar coordinate system; Align the center point of the layer to be identified with the fusion center point, and generate corresponding marker points based on the center point of the layer to be identified according to the distribution of the fusion reference points; After aligning each marker point with a blending reference point, a region blending layer is obtained.

[0008] Furthermore, the layer optimization module optimizes the layer boundaries of the obtained region blending layer and splits the boundary-optimized region blending layer to obtain the aluminum foil feature layer. The process includes: The layers to be identified, the reference image layer, and the comparison image layer in the region fusion layer are rasterized to obtain the corresponding grayscale images; The obtained layer to be identified is binarized, and the aluminum foil area is labeled according to the binarization result. Using the boundary of the aluminum foil coverage area in the reference image layer as a benchmark, the boundary of the marked aluminum foil area is optimized by the Hough transform method. Image features are extracted from the grayscale image corresponding to the optimized aluminum foil area to obtain the corresponding feature values. The aluminum foil area after image feature extraction is then split to obtain the corresponding aluminum foil feature layer.

[0009] Furthermore, using the boundary of the aluminum foil coverage area in the reference image layer as a benchmark, the process of optimizing the boundary of the marked aluminum foil area using the Hough transform method includes: Overlay the aluminum foil area of ​​the layer to be identified with the aluminum foil area of ​​the reference image layer; Obtain the overlap area of ​​the aluminum foil region in the layer to be identified and the reference image layer; The layer to be identified is rotated in rotation so that each rotation aligns the marker points in the layer to be identified with a new fusion reference point, until all fusion reference points are traversed; The state with the maximum overlap area corresponding to each rotation is recorded as the state to be optimized; Continue rotating the layer to be recognized in both directions while it is in the state of being optimized; Obtain the trend of overlapping area changes after rotation on both sides. If the overlapping area decreases after rotation on both sides, mark the state to be optimized as the final optimized state. If the overlapping area increases, continue rotating until the overlapping area begins to decrease. Then, record the state with the largest overlapping area as the final optimized state. The boundary of the reference image layer in the final optimized state is used as the reference for the boundary of the layer to be identified, and the Hough transform method is used to optimize the boundary of the layer to be identified.

[0010] Furthermore, the defect identification module performs surface defect analysis on the aluminum foil feature layer and outputs the surface defect analysis results, including: The area of ​​the layer to be identified after boundary optimization is compared with the coverage area and boundary shape of the aluminum foil in the reference image layer. If they are consistent, it means that the size and shape of the aluminum foil corresponding to the layer to be identified are qualified; otherwise, it means that it is unqualified. Based on the premise that the size and shape are qualified, the layer to be identified is divided into several unit areas; The extracted feature values ​​in each unit region are normalized to obtain the normalized feature values ​​corresponding to that unit region. Similarly, the normalized feature values ​​of the corresponding regions of the comparison image layer are obtained, and the two sets of normalized feature values ​​are compared to obtain the feature difference. The obtained feature difference is compared with a preset threshold range. If the feature difference is within the preset threshold range, it means that there is no defect in the corresponding unit area; otherwise, the corresponding unit area is marked as an abnormal area. If there are adjacent abnormal regions, the corresponding abnormal regions will be merged. The region formed by merging at least two abnormal regions will be called the extended abnormal region.

[0011] Furthermore, the process of normalizing the feature values ​​extracted from each unit region includes: The maximum, minimum, and mean values ​​of the feature values ​​corresponding to the identified features within a unit region are obtained and denoted as follows: , as well as ; The characteristic rate of change of the unit region is obtained, denoted as Tb, where: ; Set the threshold range for the rate of change to [1, a]; When Tb∈[1, a], then As the normalized characteristic value of this unit region; When Tb [1, a], then if - > - Then As the normalized characteristic value of this unit region, if - < - Then As the normalized characteristic value of this unit region, if - = - Then , It also serves as the normalized characteristic value of this unit region.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. By setting up a variety of standard environmental data and performing dynamic matching and layer fusion during detection, an adaptive image analysis benchmark is constructed, thereby reducing the fluctuation of imaging quality caused by changes in ambient lighting and significantly improving the detection accuracy and reliability in variable industrial environments. 2. By integrating center point alignment, reference point matching, and boundary optimization algorithms based on Hough transform and maximizing overlapping area, the aluminum foil contour within the detection area can be extracted, thereby determining whether the size and shape of the aluminum foil meet the standards. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0015] like Figure 1 As shown, the vision-based aluminum foil surface defect detection system includes a monitoring center and components that communicate and / or are electrically connected to the monitoring center. The data acquisition module is used to acquire image data and environmental data within the detection area; The data processing module is used to process the acquired image data to obtain a region fusion layer; The layer optimization module is used to optimize the layer boundaries of the obtained region blending layer and split the region blending layer after boundary optimization to obtain the aluminum foil feature layer. The defect identification module is used to perform surface defect analysis on the feature layer of aluminum foil and output the surface defect analysis results.

[0016] It should be further explained that, in the specific implementation process, the process by which the data acquisition module acquires image data and environmental data within the detection area includes: The data acquisition module includes a visual acquisition unit and an environmental acquisition unit; Set up a detection area, and deploy visual acquisition units at designated locations within the detection area. The shooting range of the deployed visual acquisition units completely covers the detection area. The deployed visual acquisition units acquire image data within the detection area in real time. Each corner of the detection area is equipped with a fixed marker point, and a corresponding environmental acquisition unit is deployed at the marker point. The environmental data of the corresponding location is acquired in real time through the deployed environmental acquisition unit. The environmental data includes light intensity and light angle. In the specific implementation process, before the formal aluminum foil defect detection is carried out, different standard environmental data are set for the detection area. Under different standard environmental data, the visual acquisition unit collects image data of the detection area where no aluminum foil is placed. Image data collected under different standard environmental data are recorded as the reference image layer of the corresponding standard environmental data, and a reference center and several reference points distributed in a circle with the reference center as the center are set in the reference image layer. Then, place the qualified aluminum foil in the detection area, and collect image data of the detection area through the vision acquisition unit; Image data collected under different standard environmental conditions are recorded as the corresponding standard environmental data control image layer. A control center and several control points are set within the control image layer. It should be noted that the control center and several control points are all within the coverage area of ​​the aluminum foil.

[0017] It should be further explained that, in the specific implementation process, the data processing module processes the acquired image data to obtain the region fusion layer, including the following steps: The obtained environmental data is matched with standard environmental data, and the baseline image layer and control image layer corresponding to the standard environmental data that is closest to the current environmental data are called. Construct a planar coordinate system and map the reference image layer and the comparison image layer to the planar coordinate system in sequence; Align the reference center of the reference image layer with the reference center of the control image layer, and each reference point corresponds to a control point to obtain a reference fusion layer. Record the positions of the corresponding reference center and control center as fusion center points, and the positions of the reference points and control points as fusion reference points. The obtained image data is used as the layer to be identified and mapped into a planar coordinate system; Align the center point of the layer to be identified with the fusion center point, and generate corresponding marker points based on the center point of the layer to be identified according to the distribution of the fusion reference points; After aligning each marker point with a blending reference point, a region blending layer is obtained.

[0018] It should be further explained that, in the specific implementation process, the layer optimization module optimizes the layer boundaries of the obtained region blending layer, and then splits the boundary-optimized region blending layer to obtain the aluminum foil feature layer. The process includes: The layers to be identified, the reference image layer, and the comparison image layer in the region fusion layer are rasterized to obtain the corresponding grayscale images; The obtained layer to be identified is binarized, and the aluminum foil area is labeled according to the binarization result. Using the boundary of the aluminum foil coverage area in the reference image layer as a benchmark, the boundary of the marked aluminum foil area is optimized by the Hough transform method. Image features are extracted from the grayscale image corresponding to the optimized aluminum foil area to obtain the corresponding feature values. The aluminum foil area after image feature extraction is then split to obtain the corresponding aluminum foil feature layer. It should be further noted that image feature extraction of grayscale images is a common technique used by those skilled in the art, and will not be elaborated here. In the specific implementation of this invention, the MobileNet algorithm is used, but those skilled in the art can also use other feasible algorithms according to the actual situation.

[0019] It should be further explained that, in the specific implementation process, the process of optimizing the boundary of the marked aluminum foil area using the Hough transform method, based on the boundary of the aluminum foil coverage area in the reference image layer, includes: Overlay the aluminum foil area of ​​the layer to be identified with the aluminum foil area of ​​the reference image layer; Obtain the overlap area of ​​the aluminum foil region in the layer to be identified and the reference image layer; The layer to be identified is rotated in rotation so that each rotation aligns the marker points in the layer to be identified with a new fusion reference point, until all fusion reference points are traversed; The state with the maximum overlap area corresponding to each rotation is recorded as the state to be optimized; While the layer to be recognized is in the unoptimized state, continue to rotate it in both directions; it should be noted that rotating in both directions refers to rotating the layer to be recognized clockwise and counterclockwise. Obtain the trend of overlapping area changes after rotation on both sides. If the overlapping area decreases after rotation on both sides, mark the state to be optimized as the final optimized state. If the overlapping area increases, continue rotating until the overlapping area begins to decrease. Then, record the state with the largest overlapping area as the final optimized state. The boundary of the reference image layer in the final optimized state is used as the reference for the boundary of the layer to be identified, and the Hough transform method is used to optimize the boundary of the layer to be identified.

[0020] It should be further explained that, in the specific implementation process, the defect identification module performs surface defect analysis on the aluminum foil feature layer and outputs the surface defect analysis results, including: The area of ​​the layer to be identified after boundary optimization is compared with the coverage area and boundary shape of the aluminum foil in the reference image layer. If they are consistent, it means that the size and shape of the aluminum foil corresponding to the layer to be identified are qualified; otherwise, it means that it is unqualified. Based on the premise that the size and shape are qualified, the layer to be identified is divided into several unit areas; The extracted feature values ​​in each unit region are normalized to obtain the normalized feature values ​​corresponding to that unit region. Similarly, the normalized feature values ​​of the corresponding regions of the comparison image layer are obtained, and the two sets of normalized feature values ​​are compared to obtain the feature difference. The obtained feature difference is compared with a preset threshold range. If the feature difference is within the preset threshold range, it means that there is no defect in the corresponding unit area; otherwise, the corresponding unit area is marked as an abnormal area. If there are adjacent abnormal regions, the corresponding abnormal regions will be merged, and the region formed by merging at least two abnormal regions will be called the extended abnormal region. The image portions corresponding to the abnormal area and the extended abnormal area are bounded and selected, then enlarged and uploaded to the monitoring center for technicians to confirm the specific defect type.

[0021] It should be further explained that, in the specific implementation process, the normalization process for the feature values ​​extracted from each unit region includes: The maximum, minimum, and mean values ​​of the feature values ​​corresponding to the identified features within a unit region are obtained and denoted as follows: , as well as ; The characteristic rate of change of the unit region is obtained, denoted as Tb, where: ; Set the threshold range for the rate of change to [1, a]; When Tb∈[1, a], then As the normalized characteristic value of this unit region; When Tb [1, a], then if - > - Then As the normalized characteristic value of this unit region, if - < - Then As the normalized characteristic value of this unit region, if - = - Then , It also serves as the normalized characteristic value of this unit region.

[0022] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A visual recognition-based aluminum foil surface defect detection system, characterized by, The monitoring center comprises a data acquisition module, a data processing module, a layer optimization module, and a defect identification module. The data acquisition module is configured to acquire image data and environmental data in a detection area. The data processing module is configured to process the acquired image data to obtain a region fusion layer. The layer optimization module is configured to perform boundary optimization on the obtained region fusion layer, and split the region fusion layer after the boundary optimization to obtain an aluminum foil feature layer. The defect identification module is configured to analyze surface defects of the aluminum foil feature layer and output a surface defect analysis result.

2. The visual recognition based aluminum foil surface defect detection system according to claim 1, wherein, The data acquisition module acquires image data and environmental data in a detection area in the following manner: The data acquisition module comprises a visual acquisition unit and an environmental acquisition unit. A detection area is set, and the visual acquisition unit is arranged at a designated position of the detection area, with the shooting range of the visual acquisition unit completely covering the detection area. The visual acquisition unit is used to acquire image data in the detection area in real time. Each corner of the detection area is provided with a fixed marker, and a corresponding environmental acquisition unit is arranged at the marker.

3. The visual recognition based aluminum foil surface defect detection system according to claim 2, wherein, The environmental acquisition unit is used to acquire environmental data at the corresponding position in real time, and the environmental data includes illumination intensity and illumination angle. Before formal aluminum foil defect detection, different standard environmental data are set for the detection area. Under different standard environmental data, the visual acquisition unit is used to acquire image data of the detection area without aluminum foil. The acquired image data under different standard environmental data is recorded as a reference image layer corresponding to the standard environmental data, and a reference center and a plurality of reference points distributed in a circular manner with the reference center as the center are set in the reference image layer.

4. The visual recognition based aluminum foil surface defect detection system according to claim 3, wherein, A qualified aluminum foil is placed in the detection area, and the visual acquisition unit is used to acquire image data of the detection area. The acquired image data under different standard environmental data is recorded as a comparison image layer corresponding to the standard environmental data, and a comparison center and a plurality of comparison points are set in the comparison image layer, and the comparison center and the plurality of comparison points are all within the coverage range of the aluminum foil. The data processing module processes the acquired image data to obtain a region fusion layer in the following manner: The obtained environmental data is matched with the standard environmental data. The reference image layer and the comparison image layer corresponding to the standard environmental data closest to the current environmental data are called. A plane coordinate system is constructed, and the called reference image layer and comparison image layer are sequentially mapped into the plane coordinate system. The reference center of the reference image layer and the comparison center of the comparison image layer are aligned, and each reference point corresponds to a comparison point, thereby obtaining a reference fusion layer. The corresponding positions of the reference center and the comparison center are recorded as a fusion center point, and the positions of the reference points and the comparison points are recorded as fusion reference points. The obtained image data is taken as a to-be-identified layer and mapped into the plane coordinate system. The center point of the to-be-identified layer is aligned with the fusion center point, and corresponding marker points are generated based on the center point of the to-be-identified layer according to the distribution of the fusion reference points. After each marker point is aligned with a fusion reference point, a region fusion layer is obtained.

5. The visual recognition based aluminum foil surface defect detection system according to claim 4, wherein, The layer optimization module optimizes the obtained regional fusion layer in layer boundary, and splits the regional fusion layer after boundary optimization, and the process of obtaining the aluminum foil feature layer includes: The rasterization processing is performed on the to-be-recognized layer, the reference image layer and the contrast image layer in the regional fusion layer respectively, and the corresponding gray-scale images are obtained; The obtained to-be-recognized layer is binarized, and the aluminum foil region is calibrated according to the binarization result; The boundary of the aluminum foil coverage range in the contrast image layer is taken as the reference, and the Hough transform method is used to optimize the boundary of the calibrated aluminum foil region; The gray-scale image corresponding to the aluminum foil region after optimization processing is subjected to image feature extraction, the corresponding feature value is obtained, and the aluminum foil region part after image feature extraction is split to obtain the corresponding aluminum foil feature layer.

6. The visual recognition based aluminum foil surface defect detection system according to claim 5, wherein, The boundary of the aluminum foil coverage range in the contrast image layer is taken as the reference, and the Hough transform method is used to optimize the boundary of the calibrated aluminum foil region; The aluminum foil region of the to-be-recognized layer is overlapped with the aluminum foil region of the contrast image layer; The overlapping area of the aluminum foil region after overlapping of the to-be-recognized layer and the contrast image layer is obtained; The to-be-recognized layer is rotated alternately, so that each time the to-be-recognized layer is rotated alternately, the identification point in the to-be-recognized layer is aligned with a new fusion reference point, until all the fusion reference points are traversed; The maximum overlapping state of the overlapping area corresponding to each rotation is recorded as the to-be-optimized state; The to-be-recognized layer is continuously rotated on both sides in the to-be-optimized state; The overlapping area change trend after the two-side rotation is obtained, if the overlapping areas of the two-side rotation are both reduced, the to-be-optimized state is marked as the final optimization state; If the overlapping area increases, continue to rotate until the overlapping area begins to decrease, then record the state when the overlapping area is maximum as the final optimization state; The boundary of the contrast image layer in the final optimization state is taken as the reference of the corresponding boundary of the to-be-recognized layer, so that the Hough transform method is used to complete the optimization of the boundary of the to-be-recognized layer.

7. The visual recognition based aluminum foil surface defect detection system according to claim 6, wherein, The defect recognition module analyzes the surface defects of the aluminum foil feature layer, and the process of outputting the surface defect analysis result includes: The area of the to-be-recognized layer after boundary optimization is compared with the coverage area and boundary shape of the aluminum foil in the contrast image layer, if they are consistent, it means that the size and shape of the aluminum foil corresponding to the to-be-recognized layer are qualified, otherwise, it means that they are not qualified; On the basis of the size and shape being qualified, the to-be-recognized layer is divided into a plurality of unit regions; The feature values extracted in each unit region are normalized to obtain the normalized feature values corresponding to the unit region; Similarly, the normalized feature values of the corresponding region of the contrast image layer are obtained, and the two groups of normalized feature values are compared to obtain the feature difference value; The obtained feature difference value is compared with the preset threshold range, if the feature difference value is within the preset threshold range, it means that there is no defect in the corresponding unit region, otherwise, the corresponding unit region is marked as an abnormal region; If there is an adjacent region, the corresponding abnormal region is merged, and the region merged by at least two abnormal regions is recorded as an extended abnormal region.

8. The visual recognition based aluminum foil surface defect detection system according to claim 7, wherein, The process of normalizing the extracted feature values in each unit area includes: The maximum value, the minimum value and the mean value of the feature values corresponding to the identified features in the unit area are obtained, and are denoted as , and respectively. Obtaining the feature variation rate of the unit area, denoted as Tb, wherein: ； Setting the variation rate threshold range [1, a]; When Tb∈[1, a], then as the normalized eigenvalue of this unit region; When Tb [1, a], then if - > - , then is taken as the normalized feature value of the unit region, if - < - , then is taken as the normalized feature value of the unit region, if - = - , then , are taken as the normalized feature values of the unit region simultaneously.

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