Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning.

Ivan Kuric,Miroslav Císar,Adrián Hajdučík,Milan Sága,Jaromír Klarák,Dariusz Wiecek

doi:10.3390/s21217073

Abstract

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

Highlights

In the mass production of tires, which is characterized by a large number of manufactured items, it is very difficult to conduct the final quality inspection, considering the visual and qualitative aspects of the tires before they are placed on the market
We found that the proposed tire inspection system can resolve this situation, by using an unsupervised learning approach—the DBSCAN algorithm—to separate points into clusters and detect every detectable abnormality in a specific place, according to the quality of obtained data
We described the design of a hybrid tire inspection system utilizing both

Summary

Introduction

In the mass production of tires, which is characterized by a large number of manufactured items, it is very difficult to conduct the final quality inspection, considering the visual and qualitative aspects of the tires before they are placed on the market. Qualitative aspects focus on materials, geometry, tire appearance, and final function, while the visual aspects of tires are mainly formal (but necessary) features, such as annotations, barcodes, or other features necessary to identify the product. To ensure high quality in the mass production of tires, it is necessary to obtain data from the manufacturing process and to digitize the final quality inspection before the product leaves the factory. The current trend is to apply processes from Industry 4.0, focused on automation, machine learning, sensory systems, digitization within manufacturing processes, data visualization, etc. A combination of final tire inspection and monitoring of the technological processes during production, makes it possible to capture any damaged product by classifying its defects and can even identify the possible origins of the defect

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Conclusion