Automatic defects detection using neighborhood windows features in tire X-ray images

Ensuring the production of non-defect high-quality tires is an essential part of the tire industry. X-ray inspection is one of the best methods to detect tire defects. In this paper, a new approach has been presented for detecting tire defects in X-ray images based on an entropy filter, the extraction of texture properties of patches by Local Binary Pattern, and, finally, the classification of defects using the Support Vector Machine method. In the proposed method, an entropy filter was first applied to the input. The parts of the image with different patterns were then selected as candidate regions and these regions were classified by the patch classifier. All the defects were detected and classified and, finally, the efficiency of the algorithm was evaluated. By applying this algorithm to the dataset the best performance was obtained by the LBP descriptor and the linear SVM classifier with 98\% defect location accuracy and 97\% defect detection accuracy were achieved. In order to analyze the performance, used the deep model as a classifier, thus demonstrating that the deep model has a high capability for learning complex patterns. This proposed method is sensitive to local texture and could well describe texture information, which is appropriate for most kinds of tire defects.

Automatic defects detection using neighborhood windows features in tire X-ray images Keywords:

Tire Defects Detection , Local Binary Pattern , Entropy Filter , Patch Classification , Support Vector Machine