Inspection System for Rail Surfaces Using Differential Images

Abstract

In this paper, a surface inspection system for rails is proposed. There is a lack of commercial systems and publications about surface inspection of rails. Therefore, the quality control developed by rail manufacturers depends on their own developed systems or on the equipment provided by very few sellers. Commercial systems must be configured manually by experts of seller companies. The configuration process requires large sets of all types of manufactured rails along several periods of time and the active participation of the quality engineers of the manufacturer. This is a long, cumbersome, and very expensive process. In this paper, we propose a new system that can be configured using a systematic method that can be performed by the quality engineers of the manufacturer. The proposed inspection system uses differential images of the rail surfaces obtained with a technique called the spectral images differentiation procedure. These images are processed using a computer vision algorithm that looks for variations in the pixel values among the images. In order to offer more information, a neural network approach is used for classifying detected defects into six types. The proposed system is an open solution for the inspection of rail surface, easy to implement at an affordable cost, which can be systematically configured by the quality engineers of the manufacturing company.