改进Debevec-YOLOv5的高反金属表面缺陷识别方法研究

Research on improved Debevec-YOLOv5 for surface defect detection methods of high-reflective metals

高反零件具有极强的反光性,对此类零件利用机器视觉识别时,所采图像存在高亮干扰因素,无法对零件表面缺陷进行正常检测识别。基于高动态范围成像技术提出一种改进Debevec算法与YOLOv5相结合的表面缺陷识别方法,对Debevec算法的相机响应曲线算法与图像合成算法利用粒子群算法进行改进,并利用YOLOv5对合成后的图像进行缺陷识别。对合成图像进行信息熵等客观评价指标计算,结果表明改进算法对反光件的图像合成质量优于Debevec算法与Mertens算法,由改进算法合成图像输入YOLOv5进行识别的错检率与漏检率低于Debevec算法与Mertens算法,具有实用价值。

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.