Author(s)

Ruizhi Li^1,2, Fang Tian^2,3, Shiqiang Chen^2,3*

¹School of Mathematics and Statistics, Hubei Minzu University, Enshi, China.
²Key Laboratory of Green Manufacturing of Super-Light Elastomer Materials of State Ethnic Affairs Commission (Hubei Minzu University), Enshi, China.
³School of Advanced Materials and Mechatronic Engineering, Hubei Minzu University, Enshi, China.

In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole.

Midsole, Surface Defect Detection, Image Processing, Linear Defect Detection, Block Defect Detection

Li, R. , Tian, F. and Chen, S. (2020) Research on Surface Defect Detection Method of E-TPU Midsole Based on Machine Vision. Journal of Computer and Communications, 8, 145-160. doi: 10.4236/jcc.2020.811011.