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- Title
CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection.
- Authors
Yang, Shuxin; Xie, Yang; Wu, Jianqing; Huang, Weidong; Yan, Hongsheng; Wang, Jingyong; Wang, Bi; Yu, Xiangchun; Wu, Qiang; Xie, Fei
- Abstract
Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules. Firstly, because of the potential information loss that convolutional neural networks (CNN) may encounter when dealing with miniature targets, the CBiF combines CNN with Transformer to optimize local and global features. Secondly, to address the increased computational complexity caused by the extensive use of convolutional layers, the FC uses the FasterNet block to reduce redundant computations and memory access. Lastly, the EMA is incorporated into the FC to design the EFC module and enhance feature fusion capability while ensuring the light weight of the model. CFE-YOLOv8s achieves mAP@0.5 values of 77.8% and 69.5% on the NEU-DET and GC10-DET datasets, respectively, representing enhancements of 3.1% and 2.8% over YOLOv8s, with reductions of 22% and 18% in model parameters and FLOPS. The CFE-YOLOv8s demonstrates superior overall performance and balance compared to other advanced models.
- Subjects
CONVOLUTIONAL neural networks; SURFACE defects; TRANSFORMER models; COMPUTATIONAL complexity; STEEL
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 14, p2771
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13142771