Visual Recognition of fastening bolt in Railway maintenance context by using wavelet Transform

Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required. Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the rail track searching for rail anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. The aim of this paper is to present a vision based technique to detect automatically the presence or absence of the fastening elements that fix the rail to the sleepers. The images are acquired by a digital line scan camera installed under a train. Subsequently these images are pre-processed by using wavelet transform with Haar and Daubechies approximation coefficients. The obtained coefficients are fed as input to two different neural networks: the first one identifies the bolts candidates and the second one validates the bolt recognition process. The final detecting system has been applied to a long sequence of real images showing a high reliability robustness and good performances.