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.