A Deep-Learning-Powered Near-Real-Time Detection of Railway Track Major Components: A Two-Stage Computer-Vision-Based Method

Abstract

A deep-learning-powered two-stage method for automating the inspection of railway track major components is developed in this article. Rails and two types of fasteners: 1) bolts and 2) clippers, are considered as major targeted objects in this study. Based on railway images, the developed method realizes the accurate railway track inspection via two stages: 1) the initial detection and 2) the detection calibration. At stage I, a squeeze and excitation participated YOLOv3 model is developed to generate initial detection results. A domain-logic-based hybrid model (DLHM) developed with the domain knowledge is introduced to enhance the detection performance at stage II. The DLHM consists of two modules: 1) a module for the problematic region calibration and 2) another module for the symmetric region calibration. The developed DLHM offers a high probability on inspecting overlooked or misclassified interested objects generated from stage I. The effectiveness of the proposed method for detecting railway tracks is validated with field collected railway images. An overall 95.2% mAP can be achieved via the proposed method. Four state-of-the-art deep-learning-based methods are considered as benchmarks to verify advantages of the proposed method. Via a deep comparative analytics, we show that the proposed method offers a state-of-the-art performance in the railway track major component inspection task.