关键词: 高反零件, 高动态范围成像, 表面缺陷识别, 改进 Ｄｅｂｅｖｅｃ算法

Abstract: High-reflective parts have extremely strong reflectivity. When machine vision systems are used to detect such parts, the captured images contain high brightness interference factors, making it difficult to accurately detect surface defects on the parts. Therefore, based on high dynamic range imaging technology, this paper proposes a method for surface defect recognition by combining the improved Debevec algorithm with YOLOv5. The camera response curve algorithm and image synthesis algorithm of the Debevec algorithm are improved using particle swarm optimization algorithm, and YOLOv5 is used for defect recognition on the synthesized images. Objective evaluation metrics such as information entropy are calculated for the synthesized images, and the results show that the improved algorithm has a better image synthesis quality for reflective parts than the Debevec algorithm and Mertens algorithm do. The false detection rate and missing detection rate of the improved algorithm combined with YOLOv5 to synthesize images are lower than those of the Debevec algorithm and Mertens algorithm do, indicating practical value.