Algorithm of Mask-region-based Convolution Neural Networks for Detection of Tire Sidewall Cracks

Abstract

The tire sidewall is the weakest part of the entire tire. Although the tire sidewall is not directly in contact with the ground, it often undergoes great deformation. Weather, road conditions, and driving habits can also affect the tire life. Cracking is one of the earliest signs of tire aging and deterioration. If a driver does not regularly inspect their vehicle, damage to a tire may remain undetected and an uncontrolled tire explosion may occur. In this study, we use deeplearning- based artificial intelligence computer vision to train a deep neural network model using a large number of digital images to detect tire sidewall cracks instead of traditional sensors, inspection devices, or visual inspection methods. In this study, tire sidewall crack images were preprocessed and annotated using the annotation program VGG Image Annotator (VIA). Residual network 50 (ResNet50) is used as the backbone of mask-region-based convolutional neural networks (Mask R-CNNs). The preprocessing training and test results of our dataset show that the improved Mask R-CNN has better mean accuracy (mAP) and detection accuracy than the original Mask R-CNN and Faster-R-CNN and can not only reduce inspection costs and time, but also improve the efficiency of tire crack analysis.