Application of Image Processing in Piston-connecting Rod Group Surface Defect Detection

In traditional industry, the detection of the automobile components quality is mainly completed by human eyes. Low detection accuracy, high labor consumption, and slow detection speed are important reasons for the slow development of manufacturing industry. With the continuous upgrading of optical hardware equipment such as industrial lenses and cameras, and the development of image processing technology, machine vision technology has developed rapidly in the field of image detection. Machine vision technology collects images through the imaging system, converts them into digital images by the image acquisition card, and finally detects defects through image processing software. A machine vision approach is proposed to replace traditional manual inspection methods for the detection of piston-connecting rod groups in automobile engines. The surface defects of the piston-connecting rod groups are the research object, with image processing technology as the core. Through image gray-scale transformation, de-noising, median filtering, threshold segmentation, and region of interesting extraction methods, the surface defect detection of piston-connecting rod group was ultimately achieved. It provides a research approach for similar research.