Automatic online vision-based inspection system of coupler yoke for freight trains

Abstract

, "Automatic online vision-based inspection system of coupler yoke for freight trains," J. Electron. Imaging 25(6), 061602 (2016), doi: 10.1117/1.JEI.25.6.061602. Abstract. Fault inspection plays an important role in ensuring the safe operation of freight trains. With the development of computer vision technology, the vision-based fault inspection has become one of the principal means of fault inspection. A coupler yoke is an important component of the train's connection system, and faults in this system would cause the separation of the train, leading to a serious accident. We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train. The inspection process is divided into two parts: the localization part and the recognition part. In the localization part, we propose multiple dimension features, design a fast algorithm to compute multiresolution image features, and use a linear support vector machine classifier to locate the position of the coupler yoke. In the recognition part, we propose a fast decision tree training method by prepruning noneffective features, and use Adaboost decision trees as the final fault classifier. Experimental results show that this proposed method can achieve a fault inspection rate of 98.6% while the average processing time of an image is about 98 ms, which shows our system has a high inspection accuracy and a good real-time performance.

A Unified Framework for Fault Detection of Freight Train Images Under Complex Environment

“…Liu et al [13] proposed a hierarchical fault inspection framework to detect the missing of bogie block key on freight trains with high speed and accuracy. Zheng et al [14] proposed an automatic image inspection system to inspect coupler yokes by a linear support vector machine (SVM) classifier for localization and Adaboost decision trees for recognition. However, these methods only detect one type of faults, which greatly influence their effectiveness.…”