基于视觉图像的车身漆膜微小疵病检测方法

Visual image-based method for detecting minor defects in body paint

汽车漆膜能够保护车身不受外界环境的腐蚀,但是由于生产工艺的影响,汽车漆膜在涂装时会出现多种类型的疵病,因此汽车漆膜的微小疵病检测是现在汽车生产自动化发展的关键。本文基于汽车漆膜检测的实际需求,提出了一种基于视觉图像的车身漆膜微小疵病检测方法。本方法首先对汽车漆膜图像进行多帧叠加,抑制噪声;之后利用二维小波提取图像趋势项及微小疵病的特征;最后通过局域自适应阈值和场曲优化的分割系数,实现疵病二值化分割。实验结果表明,本文方法可实现大面积图像上对0.1 mm以上疵病的有效检测,且微小疵病的召回率达到97.6%,误检率为1.3%,可达到预期检测结果。

The automobile paint film may shield the body from corrosion caused by the outside environment,but because of the effect of the manufacturing process,there will be a variety of flaws when the paint is applied.Therefore,the progress of automated automotive production depends on the identification of small flaws in paint film.Based on the actual needs of automotive paint film inspection,this paper proposes a visual image-based detection method for small defects in car paint film.This method first superimposes multiple frames of automobile paint film images to suppress noise;then uses two-dimensional wavelets to extract image trend items and characteristics of minor defects;finally,through the local adaptive threshold and the optimized segmentation coefficient of field curvature,the binary segmentation of defects is realized.The experimental results show that the method in this paper can effectively detect defects larger than 0.1 mm on large-area images,and the recall rate of small defects reaches 97.6%,and the false detection rate is 1.3%,which can achieve the expected detection results.