Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm
In this paper, we address challenges in steel surface defect inspection, such as missed detections and false detections, by proposing the GCHS-YOLO detection algorithm. Built on YOLOv8s, our approach replaces the traditional Feature Pyramid Network (FPN) in the NECK section with a multi-scale fusion...
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| Format: | Article |
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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/10798110/ |
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| author | Ruiqiang Guo Peiyong Ji Yapin Zhang Jingqi Hu Wenlong Liu Xuejian Li Min Li |
| author_facet | Ruiqiang Guo Peiyong Ji Yapin Zhang Jingqi Hu Wenlong Liu Xuejian Li Min Li |
| author_sort | Ruiqiang Guo |
| collection | DOAJ |
| description | In this paper, we address challenges in steel surface defect inspection, such as missed detections and false detections, by proposing the GCHS-YOLO detection algorithm. Built on YOLOv8s, our approach replaces the traditional Feature Pyramid Network (FPN) in the NECK section with a multi-scale fusion network (GFPN). This change improves the model’s adaptability to targets of varying scales and enhances its ability to extract key defective features.Continue to introduce the Coordinate Attention (CA) mechanism, which integrates location information to improve feature extraction, aiding in better regression and localization. This boosts the network’s ability to identify and detect defects with greater accuracy. Additionally, the Haar Wavelet Downsampling (HWD) module is incorporated to reduce the spatial resolution of feature maps while retaining important information. This not only decreases the model’s complexity but also reduces uncertainty in the extracted information.Finally, the Spatial Pyramid Dilated Convolution (SPDConv) expands the model’s perceptual field, improving feature extraction, especially for small defects. Experimental results using the NEU-DET dataset—after applying noise and Gaussian filtering—show that the GCHS-YOLO algorithm improves the mean Average Precision (mAP) by 1.2%, Precision by 0.8%, mAP@0.5:0.95 by 4.4%, and Recall by 2.8%, compared to the original YOLOv8s model. |
| format | Article |
| id | doaj-art-33ec2da49e6844b2999977e4fb110d41 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-33ec2da49e6844b2999977e4fb110d412025-01-16T00:01:50ZengIEEEIEEE Access2169-35362024-01-011219086519087510.1109/ACCESS.2024.351693210798110Steel Surface Defect Detection Based on Improved GCHS-YOLO AlgorithmRuiqiang Guo0https://orcid.org/0009-0005-3825-983XPeiyong Ji1Yapin Zhang2Jingqi Hu3Wenlong Liu4Xuejian Li5Min Li6https://orcid.org/0009-0009-8461-1290College of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaCollege of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaCollege of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaCollege of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaCollege of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaCollege of Mechanical and Vehicular Engineering, Changchun University, Changchun, ChinaKeimyung Academy, Changchun University, Changchun, ChinaIn this paper, we address challenges in steel surface defect inspection, such as missed detections and false detections, by proposing the GCHS-YOLO detection algorithm. Built on YOLOv8s, our approach replaces the traditional Feature Pyramid Network (FPN) in the NECK section with a multi-scale fusion network (GFPN). This change improves the model’s adaptability to targets of varying scales and enhances its ability to extract key defective features.Continue to introduce the Coordinate Attention (CA) mechanism, which integrates location information to improve feature extraction, aiding in better regression and localization. This boosts the network’s ability to identify and detect defects with greater accuracy. Additionally, the Haar Wavelet Downsampling (HWD) module is incorporated to reduce the spatial resolution of feature maps while retaining important information. This not only decreases the model’s complexity but also reduces uncertainty in the extracted information.Finally, the Spatial Pyramid Dilated Convolution (SPDConv) expands the model’s perceptual field, improving feature extraction, especially for small defects. Experimental results using the NEU-DET dataset—after applying noise and Gaussian filtering—show that the GCHS-YOLO algorithm improves the mean Average Precision (mAP) by 1.2%, Precision by 0.8%, mAP@0.5:0.95 by 4.4%, and Recall by 2.8%, compared to the original YOLOv8s model.https://ieeexplore.ieee.org/document/10798110/SteelsGFPNCAHWDSPDConv |
| spellingShingle | Ruiqiang Guo Peiyong Ji Yapin Zhang Jingqi Hu Wenlong Liu Xuejian Li Min Li Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm IEEE Access Steels GFPN CA HWD SPDConv |
| title | Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm |
| title_full | Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm |
| title_fullStr | Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm |
| title_full_unstemmed | Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm |
| title_short | Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm |
| title_sort | steel surface defect detection based on improved gchs yolo algorithm |
| topic | Steels GFPN CA HWD SPDConv |
| url | https://ieeexplore.ieee.org/document/10798110/ |
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