Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things
Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and oth...
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12208 |
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| author | Meixia Fu Jiansheng Wu Qu Wang Lei Sun Zhangchao Ma Chaoyi Zhang Wanqing Guan Wei Li Na Chen Danshi Wang Jianquan Wang |
| author_facet | Meixia Fu Jiansheng Wu Qu Wang Lei Sun Zhangchao Ma Chaoyi Zhang Wanqing Guan Wei Li Na Chen Danshi Wang Jianquan Wang |
| author_sort | Meixia Fu |
| collection | DOAJ |
| description | Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively. |
| format | Article |
| id | doaj-art-e9b37631cfa547af8ab17b51ce7ead2b |
| institution | OA Journals |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-e9b37631cfa547af8ab17b51ce7ead2b2025-08-20T02:21:54ZengWileyIET Signal Processing1751-96751751-96832023-05-01175n/an/a10.1049/sil2.12208Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of ThingsMeixia Fu0Jiansheng Wu1Qu Wang2Lei Sun3Zhangchao Ma4Chaoyi Zhang5Wanqing Guan6Wei Li7Na Chen8Danshi Wang9Jianquan Wang10School of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaSchool of Science and Communication Engineering University of Science and Technology Beijing Beijing ChinaSchool of Science and Communication Engineering University of Science and Technology Beijing Beijing ChinaThe Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma JapanState Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing ChinaSchool of Automation and Electrical Engineering Institute of Industrial Internet University of Science and Technology Beijing Beijing ChinaAbstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.https://doi.org/10.1049/sil2.12208artificial intelligencecomputer visionimage segmentationobject detection |
| spellingShingle | Meixia Fu Jiansheng Wu Qu Wang Lei Sun Zhangchao Ma Chaoyi Zhang Wanqing Guan Wei Li Na Chen Danshi Wang Jianquan Wang Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things IET Signal Processing artificial intelligence computer vision image segmentation object detection |
| title | Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things |
| title_full | Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things |
| title_fullStr | Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things |
| title_full_unstemmed | Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things |
| title_short | Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things |
| title_sort | region based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial internet of things |
| topic | artificial intelligence computer vision image segmentation object detection |
| url | https://doi.org/10.1049/sil2.12208 |
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