A real-time visual inspection method of fastening bolts in freight car operation

摘要

A real-time inspection of the key components is necessary for ensuring safe operation of freight car. While traditional inspection depends on the trained human inspectors, which is time-consuming and lower efficient. With the development of machine vision, vision-based inspection methods get more railway on-spot applications. The cross rod end fastening bolts are important components on both sides of the train body that fixing locking plates together with the freight car main structure. In our experiment, we get the images containing fastening bolt components, and accurately locate the locking plate position using a linear Support Vector Machine (SVM) locating model trained with Histograms of Oriented Gradients (HOG) features. Then we extract the straight line segment using the Line Segment Detector (LSD) and encoding them in a range, which constitute a straight line segment dataset. Lastly we determine the locking plate's working state by the linear pattern. The experiment result shows that the localization accurate rate is over 99%, the fault detection rate is over 95%, and the module implementation time is 2f/s. The overall performance can completely meet the practical railway safety assurance application.