Automatic detection system of shaft part surface defect based on machine vision

Lixing Jiang; Kuoyuan Sun; Fulai Zhao; Xiangyang Hao

doi:10.1117/12.2184728

22 June 2015 Automatic detection system of shaft part surface defect based on machine vision

Lixing Jiang, Kuoyuan Sun, Fulai Zhao, Xiangyang Hao

Proceedings Volume 9530, Automated Visual Inspection and Machine Vision; 95300G (2015) https://doi.org/10.1117/12.2184728
Event: SPIE Optical Metrology, 2015, Munich, Germany

Abstract

Surface physical damage detection is an important part of the shaft parts quality inspection and the traditional detecting methods are mostly human eye identification which has many disadvantages such as low efficiency, bad reliability. In order to improve the automation level of the quality detection of shaft parts and establish its relevant industry quality standard, a machine vision inspection system connected with MCU was designed to realize the surface detection of shaft parts. The system adopt the monochrome line-scan digital camera and use the dark-field and forward illumination technology to acquire images with high contrast; the images were segmented to Bi-value images through maximum between-cluster variance method after image filtering and image enhancing algorithms; then the mainly contours were extracted based on the evaluation criterion of the aspect ratio and the area; then calculate the coordinates of the centre of gravity of defects area, namely locating point coordinates; At last, location of the defects area were marked by the coding pen communicated with MCU. Experiment show that no defect was omitted and false alarm error rate was lower than 5%, which showed that the designed system met the demand of shaft part on-line real-time detection.

Citation Download Citation

Lixing Jiang, Kuoyuan Sun, Fulai Zhao, and Xiangyang Hao "Automatic detection system of shaft part surface defect based on machine vision", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300G (22 June 2015); https://doi.org/10.1117/12.2184728

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