A Visual Detection System for Rail Surface Defects

Discrete surface defects are the most common anomalies of rails and they should be carefully inspected. However, it is a challenge to detect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This paper presents an intelligent vision detection system (VDS) for discrete surface defects and focuses on two key issues of VDS: image enhancement and automatic thresholding. We propose the local Michelson-like contrast (MLC) measure to enhance rail images. MLC-based method is nonlinear and illumination independent; therefore, it notably improves the distinction between defects and background. In addition, we put forward the new automatic thresholding method-proportion emphasized maximum entropy (PEME) thresholding algorithm. PEME selects a threshold that maximizes the object entropy and meanwhile keeps the defect proportion in a low level. Our experimental results demonstrate that VDS detects the Type-II defects with a recall of 91.61% and Type-I defects with a recall of 88.53%, and the proposed MLC-based image enhancement method and PEME thresholding algorithm outperform the related well-established approaches.