RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8
Surface defect detection in chips is crucial for ensuring product quality and reliability. This paper addresses the challenge of low identification accuracy in chip surface defect detection, which arises from the similarity of defect characteristics, small sizes, and significant scale differences. W...
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MDPI AG
2025-06-01
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| author | Wenjie Tang Yangjun Deng Xu Luo |
| author_facet | Wenjie Tang Yangjun Deng Xu Luo |
| author_sort | Wenjie Tang |
| collection | DOAJ |
| description | Surface defect detection in chips is crucial for ensuring product quality and reliability. This paper addresses the challenge of low identification accuracy in chip surface defect detection, which arises from the similarity of defect characteristics, small sizes, and significant scale differences. We propose an enhanced chip surface defect detection algorithm based on an improved version of YOLOv8, termed RST-YOLOv8. This study introduces the C2f_RVB module, which incorporates RepViTBlock technology. This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. By enhancing the expressive power of deep features, we achieve a marked improvement in the identification accuracy of small defect targets. Additionally, we employ the SimAM attention mechanism, enabling the model to learn three-dimensional channel information, thereby strengthening its perception of defect characteristics. To address the issues of missed detections and false detections of small targets in chip surface defect detection, we designed a task-aligned dynamic detection head (TADDH) to facilitate interaction between the localization and classification detection heads. This design improves the accuracy of small target detection. Experimental evaluations on the PCB_DATASET indicate that our model improved the mAP@0.5 by 10.3%. Furthermore, significant progress was achieved in experiments on the chip surface defect dataset, where mAP@0.5 increased by 5.4%. Simultaneously, the model demonstrated significant advantages in terms of computational complexity, as both the number of parameters and GFLOPs were effectively controlled. This showcases the model’s balance between high precision and a lightweight design. The experimental results show that the RST-YOLOv8 model has a significant advantage in detection accuracy for chip surface defects compared to other models. It not only enhances detection accuracy but also achieves an optimal balance between computational resource consumption and real-time performance, providing an ideal technical pathway for chip surface defect detection tasks. |
| format | Article |
| id | doaj-art-28a4704b48c44e19a1628f533cf7322e |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-28a4704b48c44e19a1628f533cf7322e2025-08-20T03:16:43ZengMDPI AGSensors1424-82202025-06-012513385910.3390/s25133859RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8Wenjie Tang0Yangjun Deng1Xu Luo2College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaSurface defect detection in chips is crucial for ensuring product quality and reliability. This paper addresses the challenge of low identification accuracy in chip surface defect detection, which arises from the similarity of defect characteristics, small sizes, and significant scale differences. We propose an enhanced chip surface defect detection algorithm based on an improved version of YOLOv8, termed RST-YOLOv8. This study introduces the C2f_RVB module, which incorporates RepViTBlock technology. This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. By enhancing the expressive power of deep features, we achieve a marked improvement in the identification accuracy of small defect targets. Additionally, we employ the SimAM attention mechanism, enabling the model to learn three-dimensional channel information, thereby strengthening its perception of defect characteristics. To address the issues of missed detections and false detections of small targets in chip surface defect detection, we designed a task-aligned dynamic detection head (TADDH) to facilitate interaction between the localization and classification detection heads. This design improves the accuracy of small target detection. Experimental evaluations on the PCB_DATASET indicate that our model improved the mAP@0.5 by 10.3%. Furthermore, significant progress was achieved in experiments on the chip surface defect dataset, where mAP@0.5 increased by 5.4%. Simultaneously, the model demonstrated significant advantages in terms of computational complexity, as both the number of parameters and GFLOPs were effectively controlled. This showcases the model’s balance between high precision and a lightweight design. The experimental results show that the RST-YOLOv8 model has a significant advantage in detection accuracy for chip surface defects compared to other models. It not only enhances detection accuracy but also achieves an optimal balance between computational resource consumption and real-time performance, providing an ideal technical pathway for chip surface defect detection tasks.https://www.mdpi.com/1424-8220/25/13/3859chip surface defect detectionRST-YOLOv8feature representationsmall object detection |
| spellingShingle | Wenjie Tang Yangjun Deng Xu Luo RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 Sensors chip surface defect detection RST-YOLOv8 feature representation small object detection |
| title | RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 |
| title_full | RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 |
| title_fullStr | RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 |
| title_full_unstemmed | RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 |
| title_short | RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 |
| title_sort | rst yolov8 an improved chip surface defect detection model based on yolov8 |
| topic | chip surface defect detection RST-YOLOv8 feature representation small object detection |
| url | https://www.mdpi.com/1424-8220/25/13/3859 |
| work_keys_str_mv | AT wenjietang rstyolov8animprovedchipsurfacedefectdetectionmodelbasedonyolov8 AT yangjundeng rstyolov8animprovedchipsurfacedefectdetectionmodelbasedonyolov8 AT xuluo rstyolov8animprovedchipsurfacedefectdetectionmodelbasedonyolov8 |