Surface Defect Detection of Industrial Parts Based on YOLOv5

Abstract

Industrial product quality inspection, a crucial procedure in industrial production, is crucial in assuring product yield. Product safety and quality inspections on industrial assembly lines are predominantly manual, and there is currently a dearth of safe and dependable inspection techniques. An improved surface defect detection approach based on YOLOv5 is proposed for the problem of surface flaws in industrial components in order to improve the quality detection effect of industrial production parts. To improve the effect of dense object detection, the image features are extracted by the convolutional network and enhanced by coordinate attention. BiFPN is utilized to fuse multi-scale features in order to lower the rate of missed detection and false detection for small target samples. The detectors from the Transformer structure are added to the complex problem of fine-grained detection to improve the predictability of challenging occurrences. According to the experimental findings, on the dataset for industrial parts defects, the proposed network increases the recall of the original algorithm in abnormal classes by 5.3%, reaching 91.6%. Its inference speed can approach 95FPS, indicating an improved real-time detection performance.