Rail Defect Classification by Adaptive Self-Organizing Map

In the last years the detection and classification of surface defects of material is assuming great importance. Visual inspection can help to increase the product quality and, in particular context, the maintenance of products. The railway infrastructure is a particular field in which the periodical surface inspection of rolling plane can help an operator to prevent critical situation. We use a Gabor filter to emphasize the image regions with grey level variation. The Gabor filter h(x,y) is characterized by a frequency F, direction (theta) and parameter (sigma) . We have selected experimentally four filters with directions 0, (pi) /4, (pi) /2 and (pi) 3/4 with F equals (root)2/8 cycle/pixel and (sigma) equals 2. The problem of detection and classification is a crucial part of our work because cannot be defined an exhaustive training set of defect and no-defect images. It is necessary a method able to self-learn changes. Investigating about this problem we propose in the paper a novel Self Organized Map (SOM) network, appropriately modified, for detection and classification of rail defects. The proposed SOM network learns to classify input vectors according to how they are grouped in the input space. So, SOM learns both the distribution and topology of the input vectors belonging to the training set. During the training phase, the neurons in the layer of an SOM form some cluster or bubble representing the input training with minimum distance among them. The novelty is to modify the SOM network in order to learn continuously during the test phase.