A Comparative Study of Image Processing and Machine Learning Methods for Classification of Rail Welding Defects

Defects formed during the thermite welding process of two sections of rails require the welded joints to be inspected for quality, and the most used non-destructive method for inspection is radiography testing. However, the conventional defect investigation process from the obtained radiography images is costly, lengthy, and subjective as it is conducted manually by trained experts. Additionally, it has been shown that most rail breaks occur due to a crack initiated from the weld joint defect that was either misclassified or undetected. To improve the condition monitoring of rails, the railway industry requires an automated defect investigation system capable of detecting and classifying defects automatically. Therefore, this work proposes a method based on image processing and machine learning techniques for the automated investigation of defects. Histogram Equalization methods are first applied to improve image quality. Then, the extraction of the weld joint from the image background is achieved using the Chan–Vese Active Contour Model. A comparative investigation is carried out between Deep Convolution Neural Networks, Local Binary Pattern extractors, and Bag of Visual Words methods (with the Speeded-Up Robust Features extractor) for extracting features in weld joint images. Classification of features extracted by local feature extractors is achieved using Support Vector Machines, K-Nearest Neighbor, and Naive Bayes classifiers. The highest classification accuracy of 95% is achieved by the Deep Convolution Neural Network model. A Graphical User Interface is provided for the onsite investigation of defects.