EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips
The pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the mode...
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7619 |
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| author | Yanshun Li Shuobo Xu Zhenfang Zhu Peng Wang Kefeng Li Qiang He Quanfeng Zheng |
| author_facet | Yanshun Li Shuobo Xu Zhenfang Zhu Peng Wang Kefeng Li Qiang He Quanfeng Zheng |
| author_sort | Yanshun Li |
| collection | DOAJ |
| description | The pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the model’s size. Since modifications to enhance feature extraction in shallow networks tend to affect the speed of model inference, in order to simultaneously ensure the accuracy and speed of detection, we add the improved Fusion-Faster module to the backbone network of YOLOv7. Partial Convolution (PConv) serves as the basic operator of the module, which strengthens the feature-extraction ability of shallow networks while maintaining speed. Additionally, we incorporate the Shortcut Coordinate Attention (SCA) mechanism to better capture the location information dependency, considering both lightweight design and accuracy. The de-weighted Bi-directional Feature Pyramid Network (BiFPN) structure used in the neck part of the network improves the original Path Aggregation Network (PANet)-like structure by adding step branches and reducing computations, achieving better feature fusion. In the experiments conducted on the NEU-DET dataset, the final model achieved an 85.9% mAP and decreased the GFLOPs by 60%, effectively balancing the model’s size with the accuracy and speed of detection. |
| first_indexed | 2024-03-10T23:12:01Z |
| format | Article |
| id | doaj.art-06339021e9f54c9f9278346bed40ea41 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:12:01Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-06339021e9f54c9f9278346bed40ea412023-11-19T08:52:20ZengMDPI AGSensors1424-82202023-09-012317761910.3390/s23177619EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel StripsYanshun Li0Shuobo Xu1Zhenfang Zhu2Peng Wang3Kefeng Li4Qiang He5Quanfeng Zheng6School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, ChinaThe pursuit of higher recognition accuracy and speed with smaller model sizes has been a major research topic in the detection of surface defects in steel. In this paper, we propose an improved high-speed and high-precision Efficient Fusion Coordination network (EFC-YOLO) without increasing the model’s size. Since modifications to enhance feature extraction in shallow networks tend to affect the speed of model inference, in order to simultaneously ensure the accuracy and speed of detection, we add the improved Fusion-Faster module to the backbone network of YOLOv7. Partial Convolution (PConv) serves as the basic operator of the module, which strengthens the feature-extraction ability of shallow networks while maintaining speed. Additionally, we incorporate the Shortcut Coordinate Attention (SCA) mechanism to better capture the location information dependency, considering both lightweight design and accuracy. The de-weighted Bi-directional Feature Pyramid Network (BiFPN) structure used in the neck part of the network improves the original Path Aggregation Network (PANet)-like structure by adding step branches and reducing computations, achieving better feature fusion. In the experiments conducted on the NEU-DET dataset, the final model achieved an 85.9% mAP and decreased the GFLOPs by 60%, effectively balancing the model’s size with the accuracy and speed of detection.https://www.mdpi.com/1424-8220/23/17/7619surface defect detectionYOLOv7deep learningfeature extraction |
| spellingShingle | Yanshun Li Shuobo Xu Zhenfang Zhu Peng Wang Kefeng Li Qiang He Quanfeng Zheng EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips Sensors surface defect detection YOLOv7 deep learning feature extraction |
| title | EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips |
| title_full | EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips |
| title_fullStr | EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips |
| title_full_unstemmed | EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips |
| title_short | EFC-YOLO: An Efficient Surface-Defect-Detection Algorithm for Steel Strips |
| title_sort | efc yolo an efficient surface defect detection algorithm for steel strips |
| topic | surface defect detection YOLOv7 deep learning feature extraction |
| url | https://www.mdpi.com/1424-8220/23/17/7619 |
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