Steel Surface Defect Detection Based on Multi-Layer Fusion Networks

Abstract

Detecting defects on steel surfaces is crucial for ensuring product quality and production safety in industrial settings. Object detection using deep learning, particularly the YOLOv5 model, has become a widely adopted method for this purpose. However, the complex shapes of current steel surface defects pose challenges for precise detection, especially when using low-cost recognition devices with small resolution images. To address these challenges, we integrated the RepBi-PAN fusion network into YOLOv5, enhancing the detection capability for large targets in complex backgrounds. To mitigate issues related to the premature introduction of shallow features and decrease in Precision, we optimized the model structure by incorporating the DenseNet structure into the backbone for improved feature extraction. Additionally, we introduced the Normalized Attention Module (NAM) to enhance the detection capability for small targets. Experimental results demonstrate the effectiveness of the enhanced model, showing a 4.1% increase in mean average precision (mAP), a 3.2% improvement in precision, and a 2.4% enhancement in recall. The improved algorithm outperforms the original in complex backgrounds and recognizing small targets, addressing limitations of the Rep-Bi network. Compared to other YOLO algorithms, our approach achieves optimal values for recall and mAP while maintaining a smaller model size. In comparison with YOLOv8, the improved model surpasses all V8 models, being only 0.5% below the precision of the largest YOLOv8x model. Simultaneously, the improved model is smaller and has fewer parameters compared to all models in the YOLOv8 series, with slightly higher GFLOPs than the smaller v8 models.