Tire defect classification using a deep convolutional sparse-coding network

Abstract

Abstract Automatic quality control has garnered great interest from academia and industry in recent decades. Tire defect detection and classification are critical research topics for driving safety and improving yields. In this work, a novel deep convolutional sparse-coding network (DCScNet) is built for tire defect classification. In DCScNet, sparse coding is utilized to replace the convolution kernel of a convolutional neural network (CNN) for the extraction of features. Compared with CNN, it requires unsupervised training, reduces the subjectivity of artificially defined labels, and achieves higher classification accuracy. The experimental results validate its superior classification performance, compared with state-of-the-art classification algorithms. The classification accuracy reaches 96.8%, which meets industrial requirements.