CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds
Currently, in the domain of surface defect detection on hot-rolled strip steel, detecting small-target defects under complex background conditions and effectively balancing computational efficiency with detection accuracy presents a significant challenge. This study proposes CTL-YOLO based on YOLO11...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/4/301 |
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| author | Wenzheng Sun Na Meng Longfa Chen Sen Yang Yuguo Li Shuo Tian |
| author_facet | Wenzheng Sun Na Meng Longfa Chen Sen Yang Yuguo Li Shuo Tian |
| author_sort | Wenzheng Sun |
| collection | DOAJ |
| description | Currently, in the domain of surface defect detection on hot-rolled strip steel, detecting small-target defects under complex background conditions and effectively balancing computational efficiency with detection accuracy presents a significant challenge. This study proposes CTL-YOLO based on YOLO11, aimed at efficiently and accurately detecting blemishes on the surface of hot-rolled strip steel in industrial applications. Firstly, the CGRCCFPN feature integration network is proposed to achieve multi-scale global feature fusion while preserving detailed information. Secondly, the TVADH Detection Head is proposed to identify defects under complex textured backgrounds. Finally, the LAMP algorithm is used to further compress the network. The proposed algorithm demonstrates excellent performance on the public dataset NEU-DET, achieving a mAP50 of 77.6%, representing a 3.2 percentage point enhancement compared to the baseline algorithm. The GFLOPs is reduced to 2.0, a 68.3% decrease compared to the baseline, and the Params are reduced to 0.40, showing an 84.5% reduction. Additionally, it exhibits strong generalization capabilities on the public dataset GC10-DET. The algorithm can effectively improve detection accuracy while maintaining a lightweight design. |
| format | Article |
| id | doaj-art-c9e26229435542deaeb24f38d5dea5c5 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-c9e26229435542deaeb24f38d5dea5c52025-08-20T03:13:32ZengMDPI AGMachines2075-17022025-04-0113430110.3390/machines13040301CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex BackgroundsWenzheng Sun0Na Meng1Longfa Chen2Sen Yang3Yuguo Li4Shuo Tian5School of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaSchool of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, ChinaCurrently, in the domain of surface defect detection on hot-rolled strip steel, detecting small-target defects under complex background conditions and effectively balancing computational efficiency with detection accuracy presents a significant challenge. This study proposes CTL-YOLO based on YOLO11, aimed at efficiently and accurately detecting blemishes on the surface of hot-rolled strip steel in industrial applications. Firstly, the CGRCCFPN feature integration network is proposed to achieve multi-scale global feature fusion while preserving detailed information. Secondly, the TVADH Detection Head is proposed to identify defects under complex textured backgrounds. Finally, the LAMP algorithm is used to further compress the network. The proposed algorithm demonstrates excellent performance on the public dataset NEU-DET, achieving a mAP50 of 77.6%, representing a 3.2 percentage point enhancement compared to the baseline algorithm. The GFLOPs is reduced to 2.0, a 68.3% decrease compared to the baseline, and the Params are reduced to 0.40, showing an 84.5% reduction. Additionally, it exhibits strong generalization capabilities on the public dataset GC10-DET. The algorithm can effectively improve detection accuracy while maintaining a lightweight design.https://www.mdpi.com/2075-1702/13/4/301deep learningtarget detectiondefect recognitionCTL-YOLO |
| spellingShingle | Wenzheng Sun Na Meng Longfa Chen Sen Yang Yuguo Li Shuo Tian CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds Machines deep learning target detection defect recognition CTL-YOLO |
| title | CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds |
| title_full | CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds |
| title_fullStr | CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds |
| title_full_unstemmed | CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds |
| title_short | CTL-YOLO: A Surface Defect Detection Algorithm for Lightweight Hot-Rolled Strip Steel Under Complex Backgrounds |
| title_sort | ctl yolo a surface defect detection algorithm for lightweight hot rolled strip steel under complex backgrounds |
| topic | deep learning target detection defect recognition CTL-YOLO |
| url | https://www.mdpi.com/2075-1702/13/4/301 |
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