Detection algorithm of rail surface defects based on multifeature saliency fusion method

Abstract

Purpose Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments. Design/methodology/approach Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image. Findings On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models. Originality/value The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

Máquina De Vetores De Suporte Para Classificação De Anomalias Em Trilho a Partir De Características De Textura De Imagens Digitais

“…Em Zhai, Ma (2022) foi proposto um algoritmo de detecção de defeitos na superfície dos trilhos baseado em um método de fusão de saliência multifuncional. Esse algoritmo combina características como textura, cor e orientação para identificar áreas de interesse e detectar defeitos com maior precisão.…”

Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

“…First, the contour images of the inner and outer sides of the steel rail are captured. Then, the subpixel coordinates of the center points of the steel rail contour light stripes are extracted [22,23]. Based on the established dual-camera vision model and the calibration of the line-structured-light plane's external parameters, the steel rail contour points on the line-structured-light plane are calculated using Equation ( 11):…”