Small Sample Steel Plate Defect Detection Algorithm of Lightweight YOLOv8

The surface area of steel plate is large, and the surface defects are very common, and showing the characteristics of multi-class and small amount. Deep learning is difficult to be effectively applied to the detection of such small sample defects. In order to solve this problem, a small sample steel plate defect detection algorithm based on lightweight YOLOv8 is proposed. Firstly, an interactive data augmentation algorithm based on fuzzy search is proposed, which can effectively solve the problem that the network model cannot be effectively trained due to the lack of training samples, making it possible for deep learning to be applied in this field. Then, the LMRNet (lightweight multi-scale residual networks) network is designed to replace the backbone of YOLOv8, to achieve the lightweight of the network model and improve its portability. Finally, the CBFPN (context bidirectional feature pyramid network) and ECSA (efficient channel spatial attention) modules are proposed to make the network more effective in extracting and fusing scar features, and the Wise-IoU loss function is adopted to improve the detection performance. The comparative experimental results show that compared with the original YOLOv8 algorithm, the amount of parameters of the improved network is only 30% of the original network, the amount of calculation is 49% of the original network, the FPS is increased by 9 frame/s. The accuracy rate, recall rate and mAP have increased by 2.9, 6.5 and 5.5 percentage points respectively. Experimental results fully verify the advantages of the proposed algorithm. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.