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 VISyR, a patent pending real time Visual Inspection System for Railway maintenance, and describes how presence/absence of the fastening bolts that fix the rails to the sleepers is automatically detected. VISyR acquires images from a digital line scan camera. Data are simultaneously preprocessed according to two Discrete Wavelet Transforms, and then provided to two Multi Layer Perceptron Neural Classifiers (MLPNCs). The cross validation of these MLPNCs' avoids (practically-at-all) false positive, and revelas the presence/absence of the fastening bolts with an accuracy of 99.6% in detecting visible bolts and of 95% in detecting missing bolts. by a FPGA-based architecture performs these tasks in 8.09 ms, allowing an on-the-fly analysis of a video sequence acquired up at 200 km/h.
A Real Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal Headed Bolts Detection
A Distante;
2007
Abstract
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 VISyR, a patent pending real time Visual Inspection System for Railway maintenance, and describes how presence/absence of the fastening bolts that fix the rails to the sleepers is automatically detected. VISyR acquires images from a digital line scan camera. Data are simultaneously preprocessed according to two Discrete Wavelet Transforms, and then provided to two Multi Layer Perceptron Neural Classifiers (MLPNCs). The cross validation of these MLPNCs' avoids (practically-at-all) false positive, and revelas the presence/absence of the fastening bolts with an accuracy of 99.6% in detecting visible bolts and of 95% in detecting missing bolts. by a FPGA-based architecture performs these tasks in 8.09 ms, allowing an on-the-fly analysis of a video sequence acquired up at 200 km/h.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
