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International Journal of Nonlinear Analysis and Applications، جلد ۱۲، شماره Special Issue، صفحات ۲۴۹۳-۲۵۰۸
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عنوان انگلیسی Automatic defects detection using neighborhood windows features in tire X-ray images
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Tire Defects Detection, Local Binary Pattern, Entropy Filter, Patch Classification, Support Vector Machine
نویسندگان مقاله Yousef Sedaghat |
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Naser Parhizgar |
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Ahmad Keshavarz |
IoT and Signal Processing Research Group, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_6359_5324e4d47318216c42ddfcf2e8530768.pdf
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