An Intelligent Method for Detecting Surface Defects in Aluminium Profiles Based on the Improved YOLOv5 Algorithm

Abstract

In response to problems such as low recognition rate, random distribution of defects and large-scale differences in the detection of surface defects of aluminum profiles by other state-of-the-art algorithms, this paper proposes an improved MS-YOLOv5 model based on the YOLOv5 algorithm. First, a PE-Neck structure is proposed to replace the neck part of the original algorithm in order to enhance the model’s ability to extract and locate defects at different scales. Secondly, a multi-streamnet is proposed as the first detection head of the algorithm to increase the model’s ability to identify distributed random defects. Meanwhile, to overcome the problem of inadequate industrial defect samples, the training set is enhanced by geometric variations and image-processing techniques. Experiments show that the proposed MS-YOLOv5 model has the best mean average precision (mAP) compared to the mainstream target-detection algorithm for detecting surface defects in aluminium profiles, whereas the average single image recognition time is within 19.1FPS, meeting the real-time requirements of industrial inspection.