Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection
It is always a challenging task in the industry to detect the small printing defects under complex background. To address this problem, a defect detection algorithm based on multi-scale feature similarity evaluation and small object defect detection is proposed. Firstly, we use a Siamese neural netw...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10812738/ |
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| author | Haojie Lou Yuanlin Zheng Wenqian Chen Haiwen Liu |
| author_facet | Haojie Lou Yuanlin Zheng Wenqian Chen Haiwen Liu |
| author_sort | Haojie Lou |
| collection | DOAJ |
| description | It is always a challenging task in the industry to detect the small printing defects under complex background. To address this problem, a defect detection algorithm based on multi-scale feature similarity evaluation and small object defect detection is proposed. Firstly, we use a Siamese neural network to extract the multi-scale features of reference image and detection image. Multi-scale features are used to characterize the background information and defect object information of printing image. Secondly, to segment defect object features from the complex background features, we analyze the differences between reference image features and detection image features by calculating the similarity heat map. Further, we can get a series of candidate area with possible defects. Finally, we use a decoupled head to decode the features in the candidate region. Experimental results show that the proposed algorithm can accurately detect small printing defects in complex background and reduce the false positive rate of the detection system. |
| format | Article |
| id | doaj-art-f91e3995917544999368296dcbc62edb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f91e3995917544999368296dcbc62edb2025-01-15T00:01:23ZengIEEEIEEE Access2169-35362024-01-011219640319641210.1109/ACCESS.2024.352140310812738Multi-Scale Feature Similarity and Object Detection for Small Printing Defects DetectionHaojie Lou0https://orcid.org/0009-0004-9016-6764Yuanlin Zheng1Wenqian Chen2Haiwen Liu3Yiwu Industrial and Commercial College, Jinhua, Zhejiang, ChinaFaculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, Shaanxi, ChinaFaculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, Shaanxi, ChinaFaculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, Shaanxi, ChinaIt is always a challenging task in the industry to detect the small printing defects under complex background. To address this problem, a defect detection algorithm based on multi-scale feature similarity evaluation and small object defect detection is proposed. Firstly, we use a Siamese neural network to extract the multi-scale features of reference image and detection image. Multi-scale features are used to characterize the background information and defect object information of printing image. Secondly, to segment defect object features from the complex background features, we analyze the differences between reference image features and detection image features by calculating the similarity heat map. Further, we can get a series of candidate area with possible defects. Finally, we use a decoupled head to decode the features in the candidate region. Experimental results show that the proposed algorithm can accurately detect small printing defects in complex background and reduce the false positive rate of the detection system.https://ieeexplore.ieee.org/document/10812738/Defect detectionmulti-scale feature similarity evaluationSiamese network |
| spellingShingle | Haojie Lou Yuanlin Zheng Wenqian Chen Haiwen Liu Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection IEEE Access Defect detection multi-scale feature similarity evaluation Siamese network |
| title | Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection |
| title_full | Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection |
| title_fullStr | Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection |
| title_full_unstemmed | Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection |
| title_short | Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection |
| title_sort | multi scale feature similarity and object detection for small printing defects detection |
| topic | Defect detection multi-scale feature similarity evaluation Siamese network |
| url | https://ieeexplore.ieee.org/document/10812738/ |
| work_keys_str_mv | AT haojielou multiscalefeaturesimilarityandobjectdetectionforsmallprintingdefectsdetection AT yuanlinzheng multiscalefeaturesimilarityandobjectdetectionforsmallprintingdefectsdetection AT wenqianchen multiscalefeaturesimilarityandobjectdetectionforsmallprintingdefectsdetection AT haiwenliu multiscalefeaturesimilarityandobjectdetectionforsmallprintingdefectsdetection |