LCG-YOLO: A Real-Time Surface Defect Detection Method for Metal Components

Abstract

Surface defect inspection of metal components plays a critical role in ensuring product quality, enhancing production efficiency, and reducing costs, with particular emphasis on the detection of small-sized surface defects to ensure the safety and reliability of metal components during their usage. Existing detection methods for small size defects on the surfaces of metal components have some shortcomings, such as low precision and poor real-time performance. To solve these two problems, this paper proposes a real-time defect detection method based on improved YOLO. Firstly, the LSandGlass (LSG) module is used to replace the residual module in the backbone network, which reduces information loss, eliminates the low-resolution feature layer, and minimizes semantic loss. The network then uses a lightweight Ghost convolution at the neck to extract the network features. In addition, the convolutional block attention mechanism (CBAM) module is added to improve the detection precision of small-size defects. Finally, soft intersection over union (SIoU) is used to further enhance target detection capability. The experiment was carried out a self-made hexagonal bolt data set of typical commonly used metal components. The experimental results show that compared to the original YOLOv5, the mAP (0.5) is improved by 5.7% to 95.50%, and the reasoning FPS is improved by 21 fps to 95 fps. These results indicate that the proposed LCG-YOLO improves the real-time detection performance of metal component surface defects.

Keywords

• Surface defect detection
• LSandGlass
• CBAM
• ghost
• SIoU
• YOLOv5