ViSyR: a Vision System for Real-Time Infrastructure Inspection

Abstract

The railway maintenance is a particular application context in which the periodical surface inspection of the rolling plane is required in order to prevent any dangerous situation. Usually, this task is performed by trained personnel that, periodically, walks along the railway network searching for visual anomalies. Actually, this manual inspection is slow, laborious and potentially hazardous, and the results are strictly dependent on the capability of the observer to detect possible anomalies and to recognize critical situations. With the growing of the high-speed railway traffic, companies over the world are interested to develop automatic inspection systems which are able to detect rail defects, sleepers’ anomalies, as well as missing fastening elements. These systems could increase the ability in the detection of defects and reduce the inspection time in order to guarantee more frequently the maintenance of the railway network. This book chapter presents ViSyR: a patented fully automatic and configurable FPGA-based vision system for real-time infrastructure inspection, able to analyze defects of the rails and to detect the presence/absence of the fastening bolts that fix the rails to the sleepers. Besides its accuracy, ViSyR achieves impressive performance in terms of inspection velocity. In fact, it is able to perform inspections approximately at velocities of 450 km/h (Jump search) and of 5 km/h (Exhaustive search), with a composite velocity higher than 160 km/h for typical video sequences. Jump and Exhaustive searches are two different modalities of inspection, which are performed in different situations. This computing power has been possible thanks to the implementation onto FPGAs. ViSyR is not only affordable, but even highly flexible and configurable, being based on classifiers that can be easily reconfigured in function of different type of rails. More in detail, ViSyR's functionality can be described by three blocks: Rail Detection & Tracking Block (RDT&B), Bolts Detection Block (BDB) and Defects Analysis Block (DAB). • RD&TB is devoted to detect and track the rail head in the acquired video. So doing it strongly reduces the windows to be effectively inspected by the other blocks. It is based on the Principal Component Analysis and the Single Value Decomposition. This technique allows the detection of the coordinates of the center of the rail analyzing a single row of the acquired video sequence (and not a rectangular window having more