Tire appearance defect detection method via combining HOG and LBP features - Liu, Hongbin; Jia, Xinghao; Su, Chenhui; Yang, Hongjuan; Li, Chengdong (Frontiers in Physics, 2023)
Tire appearance defect detection based on machine vision is an effective technology to improve tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers pay attention to this technology. However, tires are characterized by single block color and various defects. It is a great challenge to accurately detect tire appearance defect. To complete the task of detecting tire defects, the 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, the HOG and LBP features of tire images are respectively extracted and used to train the Support Vector Machine (SVM) classifier. Finally, the SVM classifier calculates the predict scores of the test images via combining the HOG and LBP 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 is verified the effectiveness of detecting tire detect, and the mean accuracy is improved more than 1.6% compared to the algorithm that only uses HOG or LBP feature.