A fast surface-defect detection method based on Dense-YOLO network

Abstract

Efficient detection of surface defects is primary for ensuring product quality during manufacturing processes. To enhance the performance of deep learning-based methods in practical applications, the authors propose Dense-YOLO, a fast surface defect detection network that combines the strengths of DenseNet and you only look once version 3 (YOLOv3). The authors design a lightweight backbone network with improved densely connected blocks, optimising the utilisation of shallow features while maintaining high detection speeds. Additionally, the authors refine the feature pyramid network of YOLOv3 to increase the recall of tiny defects and overall positioning accuracy. Furthermore, an online multi-angle template matching technique is introduced based on normalised cross-correlation to precisely locate the detection area. This refined template matching method not only accelerates detection speed but also mitigates the influence of the background. To validate the effectiveness of our enhancements, the authors conduct comparative experiments across two private datasets and one public dataset. Results show that Dense-YOLO outperforms existing methods, such as faster R-CNN, YOLOv3, YOLOv5s, YOLOv7, and SSD, in terms of mean average precision (mAP) and detection speed. Moreover, Dense-YOLO outperforms networks inherited from VGG and ResNet, including improved faster R-CNN, FCOS, M2Det-320 and FRCN, in mAP.