Research on aluminum profile surface defect detection based on an improved YOLOv5 algorithm

As aluminum profiles become increasingly popular in aerospace and aircraft manufacturing, the detection and quality management of surface defects in aluminum profiles are crucial. This study enhances the YOLOv5 algorithm for detecting surface defects in aluminum profiles, using images from a company's production line. It introduces K-Means++ to the adaptive anchor box algorithm for optimal initial center selection in clustering. Additionally, C3 was replaced with C2f, and the SE attention mechanism was introduced. Experiments demonstrate that the optimized YOLOv5 algorithm performs excellently. It not only achieves faster detection speeds but also improves detection accuracy, effectively addressing the challenge of low recall rates for small and slender targets.