基于机器视觉的凸轮轴表面缺陷检测系统

Surface defect detection system for camshaft based on computer vision

传统的凸轮轴表面缺陷多为产后抽检,相比在线全检存在漏检与缺陷率上升等现象,且只能事后发现;而人工在线全检不但会使成本上升、也对人力资源提出了考验。为此实现自动实时在线全检就成为急需解决的课题。设计了基于机器视觉的凸轮轴表面缺陷在线自动检测系统。系统安装在凸轮轴生产流水线两侧,搭建特定光源,在凸轮轴移动、停止、旋转过程中通过高速相机对其表面进行图像捕获,并由工控机进行缺陷判定与定位。根据轴类表面缺陷的特征,设计了缺陷分割算法和缺陷区域标记算法,对凸轮轴表面的外伤、砂眼、研磨不良等典型缺陷进行分辨。算法可以准确提取目标缺陷区域,标记缺陷位置并统计缺陷特征对缺陷进行判定。该系统可在0.44 s每根轴的速度下,检测出凸轮轴表面直径大于1 mm的缺陷,并通过人机交互界面显示缺陷所在位置。完全可以取代产后抽检及人工在线全检,同时还可以提高检测效率与检测精度。

Compared with the on-line detection in production, traditional detection systems of camshafts＇ surface defect are mostly sampling after production in which missing rates and deficiencies rates are increased that can not be discovered until detection is completed, while full manually online detection not only increases the cost but also challenges the human resources. Therefore it is an important research subject to realize automatic real-time online detection. In this paper, a detection system of surface defect based on computer vision was designed. In this system, certain illuminants were constructed on both sides of the production line which surface images were captured by a high-speed camera and decided and located by an industrial computer while camshafts moved, stopped and rotated. For processing typical defect such as pit, pinhole porosity and rough surface, a defect segmentation algorithm and defect area marker algorithm were designed on the basis of defect features of camshaft surface. Target defect area was extracted accurately were computed for final by the algorithms, defect judgments. In this system, locations were marked as well as the defect features defect in camshaft surface which diameter is greater than 1 mm can be detected at 0.44 s/axis and locations of detect were displayed through human-machine interactive interface. Traditional sampling detection after production and full manually online detection can be completely replaced, meanwhile, the accuracy and efficiency are improved.