Deng C, Zhao Y K, Sun J L,et al.A lightweight steel surface defect detection algorithm based on YOLO-RCES[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(6):126-135.

doi:10.16186/j.cnki.1673-9787.2024030069

Received:2024/03/26

Revised:2024/10/10

Published:2026/10/09

A lightweight steel surface defect detection algorithm based on YOLO-RCES

Deng Chao, Zhao Yongkun, Sun Junling, Wang Weidong

School of Physics & Electronic Information Engineering， Henan Polytechnic University， Jiaozuo 454003， Henan， China

Abstract: Objectives To improve the deployability and detection performance of deep learning-based steel surface defect detection algorithms on edge devices， a lightweight detection algorithm， YOLO-RCES， is proposed to address the issues of large parameter size， high computational complexity， and low detection accuracy in existing methods.  Methods To reduce model complexity and enhance the utilization of low-level texture information， the RevCol network is employed to reconstruct the backbone of YOLOv8n， achieving improved detection accuracy with reduced complexity. To address the high computational cost caused by convolution stacking in the C2f module， efficient multi-scale convolution is introduced into the feature fusion network， reducing the number of parameters and floating-point operations while enriching multi-scale feature representation. Furthermore， an attention mechanism is incorporated into the improved backbone to enhance the model’s ability to capture long-range dependencies and contextual information. To alleviate the imbalance between hard and easy samples， SlideLoss is introduced to improve the detection performance for hard defects. Results Experiments on the GC10-DET dataset show that， compared with YOLOv8n， the proposed model improves precision by 2.3%， mAP@0.5 by 1.6%， and mAP @0.5∶0.95 by 1.9%， while reducing parameters by 26.7% and computational cost by 24.4%. On the NEU-DET dataset， mAP @0.5 is further improved by 1.4%， demonstrating good generalization ability.  Conclusions The proposed model achieves higher detection accuracy while maintaining a lightweight structure. Compared with existing lightweight versions of mainstream detection models and advanced lightweight steel defect detection methods， it demonstrates superior overall performance.

Key words: steel surface defect detection; lightweight; RevCol network; channel prior convolutional attention; efficient multi-scale convolution