X-ray of Tire Defects Detection via Modified Faster R-CNN

With the rapid development of deep learning model in computer vision area, it has outperformed most of traditional machine learning algorithms. Since tire factories pay much attention to defects detection of tires based on x-ray image, lots of tire x-ray image based defects detection methods are brought up. However, there are still challenges in detection accuracy. This paper put forward a novel deep learning model and modified Faster R-CNN to conduct x-ray defects detection. Some proper processing is done on x-ray image before extracting the features and detecting the defects and then adjusting the feature extractor, proposal generator and box classifier of Faster R-CNN respectively. Comprehensive experiments are carried out to testify that our proposed model is capable of achieving higher detection accuracy compared with other methods.