RCYOLO: an innovative surface defect detection model for magnetic blocks integrating RevCol and Yolov8s

Abstract

ABSTRACT The surface defect detection of magnetic blocks faces challenges such as low accuracy, high missed detection rates, and weak anti-interference capabilities. Additionally, existing models are often large in size and require significant computational resources. To address these issues, this article integrated the Yolov8 framework and designed the RCYOLO model to improve detection performance and resource utilization. Firstly, we used RevCol as the backbone, and explored its optimal number of sub-networks to reduce model parameters and complexity, as well as improve model robustness. Secondly, we removed some deep structures from Yolov8s to simplify the model, making it easier to deploy. Afterwards, we developed a more efficient feature extraction module, C2f_SA, and replaced all existing modules with it to compensate for accuracy loss caused by simplification and improve overall detection performance. Finally, we optimized reg_max to explore the best minimum value for detecting defects in magnetic blocks to reduce unnecessary computational burden. Experimental results show that compared to Yolov8s, although RCYOLO’s mAP0.5 was reduced by 0.2%, it significantly reduced the model parameters, FLOPs, and model size by 51.64%, 30.31%, and 50.22%, respectively, achieving a better balance between accuracy and resources. Moreover, RCYOLO demonstrated good detection performance compared to other improved models.