Visual recognition of hexagonal headed bolts by comparing ICA to wavelets

In this paper we present vision-based techniques to automatically detect the absence of the fastening bolts that secure the rails to the sleepers. The inspection system uses images from a digital line scan camera installed under a train. This application is part of the most general problem of object recognition. In object recognition as in supervised learning, we often extract new features from original ones for the purpose of reducing the feature space dimensions and achieving better performances. The goal of this paper is to compare two techniques within the context of the hexagonal-headed bolts recognition in railway maintenance. The first technique is Wavelets Transform (WT), the second technique is Independent Component Analysis (ICA), a new method that produces spatially localized and statistically independent basis vector. The coefficients of the new representation in the ICA and WT subspace are supplied as input to a Support Vector Machine (SVM). A SVM classifier analyses the images in order to evaluate the pre-processing technique which could give the highest rate in detecting the presence of the bolts. Results in terms of detection rate and false positive rate are given in the paper.