Steel surface defect detection based on dynamic receptive field and multi-scale features fusion

Abstract

Abstract Steel surface defect detection is vital for guaranteeing product quality in contemporary manufacturing. However, traditional steel surface defect detection algorithms often face challenges due to insufficient resilience in feature extraction under complex backgrounds. To address this, we present a framework for defect detection which boosts feature extraction through a multi-path optimization strategy, markedly enhancing both accuracy and efficiency. Firstly, we introduce a dynamic receptive field (DRF) module which employs the spatial kernel selection mechanism to enable the network for dynamic perception according to defect scales. Meanwhile, a multi-scale feature fusion (MFF) module is designed to combine shallow and deep contextual information, minimizing information loss and enhancing feature representation. Finally, comprehensive experiments on the GC10-DET, NEU-DET, and APDDD datasets show that our model achieves a mean average precision of 71.4%, 82.0%, and 68.3%, respectively, outperforming state-of-the-art methods, while keeping efficient inference and minimal computational cost for real-time industrial applications. The source codes are at https://github.com/ssjddb/DM-YOLO.git.