A GPU-based vision system for real time detection of fastening elements in railway inspection

Abstract

The railway maintenance is a particular application context required in order to prevent any dangerous situation. With the growing of the high-speed railway traffic, automatic inspection systems able to detect rail defects, sleepers' anomalies, as well as missing fastening elements, become strategic since they could increase the ability in the detection of defects and reduce the inspection time in order to guarantee more frequent maintenance of the railway network. This paper presents a patented fully automatic and configurable real-time vision system able to detect the presence/absence of the fastening bolts that fix the rails to the sleepers. It gets an accuracy of 99.9%, and, thanks to its parallel processing allowed by a Graphic Processing Unit, reaches an average throughput of 187 km/h, speeding up of about 287% the performance of a quadcore CPU implementation.