CN111523540A - Metal surface defect detection method based on deep learning - Google Patents
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Abstract
The invention provides a metal surface defect detection method based on deep learning, which comprises the following steps: step 1, determining the models of a camera and a light source according to the sizes of metal defect parts, building an image taking environment of the light source and the camera, and acquiring images; step 2, carrying out data set labeling and data set enhancement on the acquired image, inputting the image into a mixed feature pyramid network structure, and carrying out feature extraction; and 3, inputting the obtained feature map of each layer into the RPN to select a candidate region. The metal surface defect detection method based on deep learning provided by the invention realizes automatic detection and defect identification and positioning of the input image of the production line, automatic feature extraction, elimination of negative influence caused by manual feature extraction, overcoming the defect of larger influence of environmental factors in machine vision, and the mixed feature pyramid network structure has better performance for smaller targets and multi-scale problems.
Description
Technical Field
The invention relates to the technical field of metal surface defect detection, in particular to a metal surface defect detection method based on deep learning.
Background
At present, the surface quality detection of products has gained wide attention, so the detection technology has rapidly developed, and especially the detection algorithm based on deep learning has achieved many excellent results. The detection algorithm based on deep learning is higher than manual detection efficiency in the aspect of product surface quality control, the workload of inspectors can be reduced, the production cost of enterprises is reduced, the defect detection precision and positioning are more accurate, the production efficiency is improved, the automation of manufacturing industry is promoted, the intelligent development is realized, and the construction of intelligent factories is promoted. In addition, in the aspect of scientific research, deep learning belongs to a new field, theoretical results are combined with practice, so that the research results fall on the ground, better improvement is sought in practical application, deep development of related algorithms can be better promoted, research on the combination of the theoretical results of the deep learning and the practice application is developed, and the method has important research significance for the development of the deep learning.
Disclosure of Invention
The invention provides a metal surface defect detection method based on deep learning, and aims to solve the problem that machine vision is greatly influenced by environmental factors.
In order to achieve the above object, an embodiment of the present invention provides a method for detecting defects on a metal surface based on deep learning, including:
step 1, determining the models of a camera and a light source according to the sizes of metal defect parts, building an image taking environment of the light source and the camera, and acquiring images;
step 2, carrying out data set labeling and data set enhancement on the acquired image, inputting the image into a mixed feature pyramid network structure, and carrying out feature extraction;
step 3, inputting the obtained feature map of each layer into an RPN network, and selecting a candidate region;
step 4, combining the obtained feature map of each layer and the candidate region to enter ROIAlign, mapping the interesting region onto the feature map, dividing the interesting region into fixed sizes, and performing pooling operation;
and 5, inputting the output obtained by the ROI Align pooling into the convolutional layer to obtain the output of the convolutional layer, and inputting the output of the convolutional layer into the full-connection layer for classification and regression.
Wherein, the step 2 specifically comprises:
and the data set is marked by adopting a Labelimg tool for on-line processing, and the data set is enhanced by adopting rotation, defect image counterfeiting and scaling and random darkness.
Wherein, the step 2 further comprises:
and fusing the obtained feature graphs on the basis of the mixed feature pyramid network.
Wherein, the step 3 specifically comprises:
16x16 256-dimensional feature vectors are obtained by convolution with 3x3, an 18x16x16 feature map and a 36x16x16 feature map are obtained by convolution with 1x1 twice, and finally 16x16x9 results are obtained, wherein each result has four coordinates and two scores, and then the candidate frames are obtained after processing through the set size of Anchors.
Wherein, the step 4 specifically comprises:
the feature representation is integrated and output as a value.
Wherein, the step 5 specifically comprises:
and (5) forming a complete graph by the local features obtained by the convolutional layers through a weight matrix, and then classifying.
The scheme of the invention has the following beneficial effects:
the metal surface defect detection method based on deep learning in the embodiment of the invention realizes automatic detection and defect identification and positioning of the input image of the production line, automatic feature extraction, elimination of negative influence caused by manual feature extraction, overcoming the defect of large influence of environmental factors in machine vision, and the mixed feature pyramid network structure has better performance for smaller targets and multi-scale problems.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a deep learning model according to the present invention;
FIG. 3 is an architectural diagram of a hybrid signature pyramid network of the present invention;
FIG. 4 is a graph (1) of the average accuracy of the present invention;
FIG. 5 is a graph (2) showing the average accuracy of the expression effect of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following detailed description is made with reference to the accompanying drawings and specific embodiments.
The invention provides a metal surface defect detection method based on deep learning, aiming at the problem that the existing machine vision is greatly influenced by environmental factors.
As shown in fig. 1 to 5, an embodiment of the present invention provides a method for detecting defects on a metal surface based on deep learning, including: step 1, determining the models of a camera and a light source according to the size of a metal defect part, constructing an image taking environment of the light source and the camera, and acquiring an image; step 2, carrying out data set marking and data set enhancement on the acquired image, inputting the image into a mixed feature pyramid network structure, and carrying out feature extraction; step 3, inputting the obtained characteristic graph of each layer into an RPN network, and selecting candidate regions; step 4, combining the obtained feature map of each layer and the candidate region to enter ROIAlign, mapping the region of interest to the feature map, dividing the region of interest into fixed sizes, and performing pooling operation; and 5, inputting the output obtained by the ROIAlign pooling into the convolutional layer to obtain the output of the convolutional layer, and inputting the output of the convolutional layer into the full-connection layer for classification and regression.
The metal surface defect detection method based on deep learning provided by the embodiment of the invention takes a Foshan aluminum profile production line as a research, and finds that the existing production line mainly adopts manual detection, but the manual detection has low efficiency, the evaluation standard is difficult to accurately quantify, and the comprehensive automatic production is not realized, a large number of defect pictures are collected on site, and after data set analysis is carried out, the metal surface defect detection method based on deep learning selects a deep learning model as a starting point. In the detection of defects on a metal surface, compared with a smooth surface (such as an optical component), a photo of the metal surface may be prone to problems such as uneven illumination, strong reflection, background noise, stains and the like, and thus detection using a machine vision method is difficult. In addition, due to the limitation of production environment, the surface of the product is easy to generate pollutants, which is inevitable, and the influence of foreign matters such as dust and fiber is also included. In conclusion, for the detection of the metal surface defects, the deep learning model has better advantages in the metal surface defect detection.
The metal surface defect detection method based on deep learning in the embodiment of the invention is roughly divided into two parts, namely, a first stage, data set acquisition and picture preprocessing; and in the second stage, inputting the pictures into the mixed characteristic pyramid network for repeated training and optimization to obtain a network model with higher performance.
Wherein, the step 2 specifically comprises: and the data set is marked by adopting a Labelimg tool for on-line processing, and the data set is enhanced by adopting rotation, counterfeit defect images, zoom and random darkness.
Wherein, the step 2 further comprises: and fusing the obtained feature graphs on the basis of the mixed feature pyramid network.
In the method for detecting metal surface defects based on deep learning according to the embodiment of the invention, on the basis of the feature pyramid network, the feature graph obtained by fusion is cut to the same size in an interpolation or maximum pooling manner, then the feature graphs of the same size are added to obtain an average feature graph, and the average feature graph is cut to the size of the previous feature graph by up-down sampling or convolution.
Wherein, the step 3 specifically comprises: 16x16 256-dimensional feature vectors are obtained by convolution with 3x3, an 18x16x16 feature map and a 36x16x16 feature map are obtained by convolution with 1x1 twice, and finally 16x16x9 results are obtained, wherein each result has four coordinates and two scores, and then the candidate frames are obtained after processing through the set size of Anchors.
Wherein, the step 4 specifically comprises: the feature representation is integrated and output as a value.
Wherein, the step 5 specifically comprises: and (5) forming a complete graph by the local features obtained by the convolutional layers through a weight matrix, and then classifying.
The metal surface defect detection method based on deep learning described in the above embodiments of the present invention needs to train a deep learning model, continuously optimizes the deep learning model in the training process, for example, soft-NMS is used to replace NMS, analyzes the distribution of real frames in a training set by k-means clustering algorithm to obtain the anchors size of a data set, and replaces the final convolution layer with feasible convolution, so as to adapt to geometric deformation well, extract more accurate positions of features, and train and adjust parameter settings repeatedly until the requirements of robustness and accuracy of a production line are met.
The metal surface defect detection method based on deep learning of the embodiment of the invention comprises the following specific implementation steps: and acquiring pictures, wherein the total number of all types of defect pictures is 300, and the total number of the defect pictures is 3000, and in order to improve the correctness of a detection result, the number of each type of defect pictures reaches 3000 by adopting a data augmentation mode. And training the feature extraction part by combining the defect picture and the normal picture. The backbone networks used were pre-trained on ImageNet, with a learning rate of 0.001 after addition of the modified mixed feature pyramid network structure, and a batch size of 16, and for evaluation the detector was run on a single Titan X GPU with CUDA9 and CUDNN7, with a batch size of 1. In the training process, the deep learning model is optimized: the K-means clustering method replaces the fixed anchor size, approximately 1 percentage point is increased, the size is changed, and the proportion is unchanged. The ROI Align is used for replacing ROI Pooling, the ROI Align is a regional characteristic gathering mode, the problem of region mismatching caused by twice quantization in ROI Pooling operation is well solved, and experiments show that the ROI Pooling is replaced by the ROI Align in a detection task, so that the accuracy of a detection model can be improved. The reason that the last block of the original result-101 structure is changed into the deformable convolution is that in the implementation of the deformable convolution, a deviation needs to be learned based on the previous features, the previous features are strong enough to ensure that the deviation cannot be learnt disorderly, so that the last block is changed, the general flow of the metal surface defect detection method based on the deep learning is shown in fig. 1, the appearance effect of the metal surface defect detection method based on the deep learning is shown in fig. 4, the defects of scratch, missing bottom, orange peel and dirty points in fig. 4 are obviously improved, and the average accuracy reaches 86.31%.
Continuously optimizing the metal surface defect detection method based on deep learning through improvement, so that the overall average accuracy of the metal surface defect detection method based on deep learning is continuously improved, introducing a method for forging a defect image into a data set reinforcing part, analyzing the threshold distribution of the defect image, and eliminating the dependence of the method on the characteristic surrounding environment by adding small squares or random-shaped graphs; in the emotional part of the data set, a mode of mixing two images is tried, the final detection result is not friendly in consideration of excessive interference, so that the mode is abandoned, and meanwhile, in the training process, the soft-NMS is used for replacing the NMS. Fig. 5 shows the final performance of the metal surface defect detection method based on deep learning on a data set, the overall performance is good, the average accuracy of three defects, namely the mottled defect, the angular position bottom leakage defect and the pit formation in fig. 5 can reach 100%, the overall average accuracy is improved to 88.91%, and is improved by about 2%.
The metal surface defect detection method based on deep learning in the embodiment of the invention realizes automatic detection and defect identification and positioning of the input image of the production line, automatic feature extraction, elimination of negative influence caused by manual feature extraction, overcoming the defect of large influence of environmental factors in machine vision, and the mixed feature pyramid network structure has better performance for smaller targets and multi-scale problems.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should be construed as the protection scope of the present invention.
Claims (6)
1. A metal surface defect detection method based on deep learning is characterized by comprising the following steps:
step 1, determining the models of a camera and a light source according to the sizes of metal defect parts, building an image taking environment of the light source and the camera, and acquiring images;
step 2, carrying out data set labeling and data set enhancement on the acquired image, inputting the image into a mixed feature pyramid network structure, and carrying out feature extraction;
step 3, inputting the obtained characteristic graph of each layer into an RPN network, and selecting candidate regions;
step 4, combining the obtained feature map of each layer and the candidate region to enter ROIAlign, mapping the region of interest to the feature map, dividing the region of interest into fixed sizes, and performing pooling operation;
and 5, inputting the output obtained by the ROIAlign pooling into the convolutional layer to obtain the output of the convolutional layer, and inputting the output of the convolutional layer into the full-connection layer for classification and regression.
2. The method for detecting defects on a metal surface based on deep learning of claim 1, wherein the step 2 specifically comprises:
and the data set is marked by adopting a Labelimg tool for on-line processing, and the data set is enhanced by adopting rotation, counterfeit defect images, zoom and random darkness.
3. The method for detecting the defects on the metal surface based on the deep learning as claimed in claim 2, wherein the step 2 further comprises:
and fusing the obtained feature graphs on the basis of the mixed feature pyramid network.
4. The method for detecting defects on a metal surface based on deep learning of claim 3, wherein the step 3 specifically comprises:
obtaining 16x16 256-dimensional feature vectors by using convolution of 3x3, obtaining an 18x16x16 feature map and a 36x16x16 feature map by using convolution of 1x1 twice, finally obtaining 16x16x9 results, wherein each result has four coordinates and two scores, and obtaining a candidate frame after processing through the set size of Anchors.
5. The method for detecting defects on a metal surface based on deep learning of claim 4, wherein the step 4 specifically comprises:
the feature representation is integrated and output as a value.
6. The method for detecting defects on a metal surface based on deep learning of claim 5, wherein the step 5 specifically comprises:
and (5) forming a complete graph by the local features obtained by the convolutional layers through a weight matrix, and then classifying.
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| CN113239786A (en) * | 2021-05-11 | 2021-08-10 | 重庆市地理信息和遥感应用中心 | Remote sensing image country villa identification method based on reinforcement learning and feature transformation |
| CN113658108A (en) * | 2021-07-22 | 2021-11-16 | 西南财经大学 | Glass defect detection method based on deep learning |
| WO2022160170A1 (en) * | 2021-01-28 | 2022-08-04 | 东莞职业技术学院 | Method and apparatus for detecting metal surface defects |
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| CN112184619A (en) * | 2020-08-25 | 2021-01-05 | 华北电力大学（保定） | Metal part surface defect detection method based on deep learning |
| CN112508090A (en) * | 2020-12-04 | 2021-03-16 | 重庆大学 | External package defect detection method |
| WO2022160170A1 (en) * | 2021-01-28 | 2022-08-04 | 东莞职业技术学院 | Method and apparatus for detecting metal surface defects |
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| CN113239786A (en) * | 2021-05-11 | 2021-08-10 | 重庆市地理信息和遥感应用中心 | Remote sensing image country villa identification method based on reinforcement learning and feature transformation |
| CN113658108A (en) * | 2021-07-22 | 2021-11-16 | 西南财经大学 | Glass defect detection method based on deep learning |
| WO2023168984A1 (en) * | 2022-03-09 | 2023-09-14 | 三门三友科技股份有限公司 | Area-array camera-based quality inspection method and system for cathode copper |
| CN114863094A (en) * | 2022-05-31 | 2022-08-05 | 征图新视(江苏)科技股份有限公司 | Industrial image region-of-interest segmentation algorithm based on double-branch network |
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