An automatic inspection system for the hexagonal headed bolts detection in railway maintenance

Rail inspection is a very important task in railway maintenance for traffic safety and for preventing dangerous situations. Railway infrastructure monitoring is a particular application context in which the periodical inspection of rail rolling plane is required. Usually the inspection of the rail is operated manually. A trained human operator walks along the track, searching for visual anomalies. Actually, the described monitoring ways are not more acceptable for their slowness and for the lack of objectivity. In fact, the results are constrained to the ability of the observer to recognize critical situations. This paper presents a vision-based technique to detect, automatically, the presence or absence of the fastening bolts that fix the rails to the sleepers. The inspection system acquires images by a digital line scan camera installed under a train. The images are pre-processed by using wavelet transform with Haar and Daubechies approximation coefficients. We have used two pre-processing techniques in order to reduce the computational time and speed up the whole fastening bolt detecting process. These coefficients are supplied as input to two different neural networks, in this way the first neural network identify the fastening bolt candidates and the second neural network validates the recognition process of the bolt. The final detecting system has been applied to a long sequence of real images showing a high reliability, robustness and good performance in term of computational speed.