The defects such as sidewall foreign matter, crown foreign body, air bubble, crown split and sidewall root opening that appear in the process of tire production will affect the use of tires after leaving factory, so it is necessary to carry out nondestructive testing on each tire before leaving the factory. In order to achieve automatic detection of tire defects in industry, an automatic tire defect detection method based on improved Faster Region-Convolutional Neural Network (Faster R-CNN) was proposed. Firstly, at the preprocessing stage, the gray level of tire image was stretched by the histogram equalization method to enhance the contrast of the dataset, resulting in a significant difference between gray values of the image target and the background. Secondly, to improve the accuracy of position detection and identification of tire defects, the Faster R-CNN structure was improved. That is the convolution features of the third layer and the convolution features of the fifth layer in ZF (Zeiler and Fergus) convolutional neural network were combined together and output as the input of the regional proposal network layer. Thirdly, the Online Hard Example Mining (OHEM) algorithm was introduced after the RoI (Region-of-Interesting) pooling layer to further improve the accuracy of defect detection. Experimental results show that the tire X-ray image defects can be classified and located accurately by the improved Faster R-CNN defect detection method with average test recognition of 95.7%. In addition, new detection models can be obtained by fine-tuning the network to detect other types of defects..