<title>Inspection of tire tread defects using image processing and pattern recognition techniques</title>

Abstract

An image acquisition and processing algorithm for inspection of tire treads has been developed. The tire treads are flat strips of black rubber material used as the main component in retreading automobile tires. These treads have a complex molded design on one side (DESIGN SIDE) and a fiat surface on the other side (see Figure 1). The inspection of the Design Side of the tread is one of the key operations in the tread fabrication process impacting quality and consistency of the final product. Increasing production rates and rigorous quality control demands put forth a requirement for the automation of the inspection operation. The main objective of the automated inspection system is to provide on-line, 100% inspection of the product for specified types of potential defects. Additional system benefit will include full access to the real-time process-control data, which can be used as process feedback parameters and allow implementation of advanced process-control techniques. This paper will discuss development of the main optical inspection algorithms utilized in the system design. The algorithms described in this paper were tested in the laboratory prototype of the inspection system and will be implemented in the final production system.

Vision-Based Inspection of Tyre Tread Depth

“…In addition to the research presented above, some researchers have been involved in research on how to detect tyre tread defects. For example, Chen et al [13] inspected tyre tread defects by using image processing and pattern recognition methods, while Xiang et al [14] used the dictionary-based representation to detect tyre defects. In this paper, an automatic system for tyre tread inspection based on image processing and support-vector machine (SVM) classification is proposed.…”

Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires

“…A comprehensive scenario of the production processes may be shown by using either fishbone diagrams or a roof-shaped L-matrix diagrams, which immediately show information about defects [9] and enhance the production according to the ISO 9001:2015 quality standards. Some authors [10] have adopted IoT techniques to identify tire defects. Different approaches for the detection and classification of defects have been recently investigated in various fields, including generative adversarial networks [11,12], variational autoencoders [13][14][15][16][17] and iterative energy-based projection [18].…”

A New Approach for Machined Parts Inspection

“…Vision systems have been used to detect surface defects on parts such as groove cracks [4] and some vision systems are used for dimensional measurement [5]. In a vision system, video cameras are used to capture, from one or more angles, the images of the part under inspection and a computer is used for signal processing and analysis.…”