A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection
To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters use...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/769 |
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| author | Liefa Liao Chao Song Shouluan Wu Jianglong Fu |
| author_facet | Liefa Liao Chao Song Shouluan Wu Jianglong Fu |
| author_sort | Liefa Liao |
| collection | DOAJ |
| description | To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters used for defect detection, particularly the interplay between feature extraction, fusion, and bounding box regression, which often leads to inefficiencies in traditional methods. YOLOv10n-SFDC incorporates advanced elements such as the DualConv module, SlimFusionCSP module, and Shape-IoU loss function, improving feature extraction, fusion, and bounding box regression to enhance accuracy. Testing on the NEU-DET dataset shows that YOLOv10n-SFDC achieves a mean average precision (mAP) of 85.5% at an Intersection over Union (IoU) threshold of 0.5, a 6.3 percentage point improvement over the baseline YOLOv10. The system uses only 2.67 million parameters, demonstrating efficiency. It excels in identifying complex defects like ’rolled in scale’ and ’inclusion’. Compared to SSD and Fast R-CNN, YOLOv10n-SFDC outperforms these models in accuracy while maintaining a lightweight architecture. This system excels in automated inspection for industrial environments, offering rapid, precise defect detection. YOLOv10n-SFDC emerges as a reliable solution for the continuous monitoring and quality assurance of steel surfaces, improving the reliability and efficiency of steel manufacturing processes. |
| format | Article |
| id | doaj-art-ac754c2a9b734de6b52e7aa2e7ce20f4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-ac754c2a9b734de6b52e7aa2e7ce20f42025-08-20T02:12:29ZengMDPI AGSensors1424-82202025-01-0125376910.3390/s25030769A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect DetectionLiefa Liao0Chao Song1Shouluan Wu2Jianglong Fu3Jiangxi University of Science and Technology, Nanchang 330000, ChinaJiangxi University of Science and Technology, Nanchang 330000, ChinaJiangxi University of Science and Technology, Nanchang 330000, ChinaBig Data Technology Innovation Center of Zhangjiakou, Zhangjiakou 075000, ChinaTo address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters used for defect detection, particularly the interplay between feature extraction, fusion, and bounding box regression, which often leads to inefficiencies in traditional methods. YOLOv10n-SFDC incorporates advanced elements such as the DualConv module, SlimFusionCSP module, and Shape-IoU loss function, improving feature extraction, fusion, and bounding box regression to enhance accuracy. Testing on the NEU-DET dataset shows that YOLOv10n-SFDC achieves a mean average precision (mAP) of 85.5% at an Intersection over Union (IoU) threshold of 0.5, a 6.3 percentage point improvement over the baseline YOLOv10. The system uses only 2.67 million parameters, demonstrating efficiency. It excels in identifying complex defects like ’rolled in scale’ and ’inclusion’. Compared to SSD and Fast R-CNN, YOLOv10n-SFDC outperforms these models in accuracy while maintaining a lightweight architecture. This system excels in automated inspection for industrial environments, offering rapid, precise defect detection. YOLOv10n-SFDC emerges as a reliable solution for the continuous monitoring and quality assurance of steel surfaces, improving the reliability and efficiency of steel manufacturing processes.https://www.mdpi.com/1424-8220/25/3/769steel plate defect detectiondeep learningYOLOv10n-SFDC modelYOLOv10surface detection |
| spellingShingle | Liefa Liao Chao Song Shouluan Wu Jianglong Fu A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection Sensors steel plate defect detection deep learning YOLOv10n-SFDC model YOLOv10 surface detection |
| title | A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection |
| title_full | A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection |
| title_fullStr | A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection |
| title_full_unstemmed | A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection |
| title_short | A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection |
| title_sort | novel yolov10 based algorithm for accurate steel surface defect detection |
| topic | steel plate defect detection deep learning YOLOv10n-SFDC model YOLOv10 surface detection |
| url | https://www.mdpi.com/1424-8220/25/3/769 |
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