Detection of surface defects on steel balls using image processing technology

Jian-min Zhou; Yang Yang

doi:10.1117/12.819620

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31 December 2008 Detection of surface defects on steel balls using image processing technology

Jian-min Zhou, Yang Yang

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Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 713028 (2008) https://doi.org/10.1117/12.819620
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China

Abstract

Manual methods of surface defect detection and recognition on steel balls which have large workload and poor reliability are widely used in domestic manufactories. The defects of steel balls' surface have several primary categories, including pitting, scuffing, scratch and nicks. An image processing technology for automatic detection of surface defects on steel balls is proposed. The first step is image segmentation. According to defects' images which have different gray-level histogram, iterative method and mode method are adopted to make binarization. Then, a connected component labeling algorithm to sign the connected region in binary image is also presented. The following and a crucial step was feature extraction. General geometry features and moment invariant features of every connected region are calculated for recognition. Eventually, BP neural network is an efficient approach to recognize classification. Experiment show that mainly 95 percent of the surface defect categories have been classified correctly.

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Jian-min Zhou and Yang Yang "Detection of surface defects on steel balls using image processing technology", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 713028 (31 December 2008); https://doi.org/10.1117/12.819620

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