Fastening Bolts Recognition in Railway Images by Independent Component Analysis

P.L. Mazzeo, N. Ancona, E. Stella, and A. Distante (Italy)


Object Recognition, Independent Component Analysis(ICA), Principal Component Analysis (PCA), SupportVector Machine (SVM).


This paper presents a vision-based technique 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 dimensions of feature space and achieving better performances. The used technique is a 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 subspace are supplied as input to a Support Vector Machine (SVM). A SVM classifier analyses the images in order to evaluate the classify capability of the ICA pre-processing technique. Then these results have been compared with ones obtained by Principal Component Analysis (PCA) pre-processing. Results in terms of detection rate and false positive rate are given in the paper.

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