Tire appearance defect detection method via combining HOG and LBP features

Abstract

Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.