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.

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Rail inspection is an essential task in railway maintenance and is periodically needed. Inspection is manually operated by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity. This paper deals with a patented Visual Inspection System for Railway maintenance, devoted to different tasks. Here, its Rail Detection & Tracking Block (RD&TB) is presented.RD&TB detects and tracks, into the acquired video sequence the rail head, by this way, notably reducing the area to be analyzed and inspected by other modules of VISyR. Thanks to its hardware implementation, RD&TB performs its task in 5.71 μs with an accuracy of 98.5%, allowing an on-the-fly analysis of a video sequence acquired up at 190 km/h. RD&TB is highly flexible and configurable, since it is based on classifiers that can be easily reconfigured in function of different type of rails.

Rail inspection is a very important task in railway maintenance and it is periodically needed for preventing dangerous situations. Inspection is operated manually by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity, because the results are related to the ability of the observer to recognize critical situations.The paper presents a prototypal FPGA-based architecture which automatically detects presence/absence of the fastening bolts that fix the rails to the sleepers.A simple predicting algorithm, exploiting the geometry of the railways, extracts, from the long video sequence acquired by a digital line scan camera, few windows where the presence of bolts is expected. These windows are preprocessed according to a Haar Transform and then provided to a Multi Layer Perceptron Neural Classifiers (MLPNCs) which reveals the presence/absence of the fastening bolts with an accuracy of 99.6% in detecting visible bolts and of 95% in detecting missing bolts. A FPGA-based architecture performs these tasks in 13.29 µs, allowing an on-the-fly analysis of a video sequence acquired up at 190 km/h.

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