A Visual Approach for Detecting Tyre Flaws That Makes Use of The Curvelet Characteristic
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Research Parks Publishing LLC
Abstract
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Automatic flaw identification is a crucial and difficult subject in the realm of industrial quality inspection for many different types of businesses. After the tyres have been manufactured, we use the curvelet transform to do an analysis on each tyre in order to locate imperfections on the tire's outer surface. In this paradigm, deep image features can be learned, and then later used for detection, classification, and retrieval tasks using bigger coefficients in the sub-highest frequency band represented by the curvelet feature. Curvelets are a type of wavelet transform that are used to represent curvelets. We investigate image categorization challenges using deep learning with the goal of applying our findings to practical, real-world applications. The findings of the experiments demonstrate that the method that was developed is capable of accurately locating and segmenting flaws in tyre images.
Keywords
Tyre Flaws, Tyre defects, Threshold Calculation, Curvelet Characteristic, Local edge estimation.