Detection of irregular small defects on metal base surface of infrared laser diode based on deep learning

The rapid development of deep learning has promoted the research progress in the field of visual object detection. In metal surface irregular small defect object detection, the traditional metal surface defect detection methods were not effective in detecting small and irregular defects. Therefore, how to improve the detection accuracy of small irregular defects on metal surfaces is a hot and difficult research problem. In this study, we propose a deep learning-based method for detecting small irregular defects on metal surfaces. Firstly, we make our own infrared laser diode metal base surface defect dataset to fill the gap of the dataset. Secondly, a new shallow feature extraction layer based on You Only Look Once (YOLO) v5s (an improved YOLO model) is designed for detecting irregular small object defects in the metal surface defect dataset. Thirdly, an attention mechanism was introduced into the network model to enhance the network’s ability to extract features of small target defects. The improved model was trained and evaluated on the laser diode metal base surface defect detection dataset, and the results showed that the accuracy of the improved algorithm improved by 3.8% over the original detection algorithm. The detection accuracy also achieves very significant results when compared with other excellent object detection algorithms.