Research on Surface Defect Detection of the Connecting Rod Based on Machine Vision

Zheng Bin, Mo Shaoxiong
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Abstract

In recent years, with the development of the automobile industry increasingly rapid, the engine production has also undergone tremendous changes, which not only requires mass production, but also pays attention to rapid production. As the power transmission link between engine crankshaft and piston, the importance of connecting rod quality in engine production is beyond doubt. In order to meet the requirements of engine batch and rapid production, machine vision inspection technology is essential in engine production. This paper takes the connecting rod image as the research object. First, according to the requirements of industrial production, the hardware device of the connecting rod surface defect system is designed. Then a set of automatic detection systems based on machine vision is designed to detect the surface defects of the connecting rod without damage. The system uses CCD camera to improve the speed of the detection system and reduce the performance requirement, making it easier to realize defect detection under existing conditions. The image segmentation threshold is automatically selected, and the connecting rod information is extracted from the image according to the actual threshold value, and the information in the connecting rod image is obtained to realize the defect detection. Based on the connecting rod image obtained by camera, the image noise is removed, and the defect on the connecting rod surface is segmented by binarization with the threshold value automatically selected to realize the automatic defect detection.
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基于机器视觉的连杆表面缺陷检测研究
近年来,随着汽车工业的日益迅猛发展,发动机的生产也发生了巨大的变化,不仅要求大批量生产,而且注重快速生产。连杆作为发动机曲轴与活塞之间的动力传递环节,其质量在发动机生产中的重要性是毋庸置疑的。为了满足发动机批量、快速生产的要求,机器视觉检测技术在发动机生产中是必不可少的。本文以连杆图像为研究对象。首先,根据工业生产的要求,设计连杆表面缺陷系统的硬件装置。然后设计了一套基于机器视觉的自动检测系统,对连杆表面缺陷进行无损检测。该系统采用CCD摄像机,提高了检测系统的速度,降低了性能要求,使得在现有条件下更容易实现缺陷检测。自动选择图像分割阈值,并根据实际阈值从图像中提取连杆信息,获得连杆图像中的信息,实现缺陷检测。在相机获取连杆图像的基础上,去除图像噪声,对连杆表面缺陷进行二值化分割,并自动选择阈值,实现缺陷自动检测。
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