MtlrNet: An Effective Deep Multitask Learning Architecture for Rail Crack Detection

Abstract

Studying automated rail surface defect detection methods instead of manual inspections can not only save time and cost, but also increase the safety of railway transport. Consequently, many methods have been developed which utilize convolutional neural networks (CNN) in order to automatically detect rail surface defects. In these works, however, little attention has been paid to the detection of rail surface crack defects. The rail surface crack defect is actually an early defect that, if not detected and maintained in time, can result in worse defects. In order to detect crack defects on the rail surface rapidly and accurately, we propose an effective deep multi-task learning architecture. Our architecture contains not only a segmentation decoder in order to segment the crack defect region on the rail, but also an object detection decoder to detect rail objects. During the training stage, as an auxiliary task, the rail object detection can transfer its learned knowledge to the rail crack region segmentation task, so as to improve the accuracy of the model. During the testing phase, the rail object detection task does not require inference, which allows the model to have a fast inference speed. Our model has been validated using the challenging Rail-5k dataset, achieving a balance between accuracy and speed.