Tire Defect Detection Using Fully Convolutional Network

A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN) for detecting defects in tire X-ray images. Owing to the capability of pixel-wise prediction of FCN, the location, and segmentation of defects are completed simultaneously. The network architecture used in the method mainly consists of three phases. The first phase is a traditional deep network, which is used to extract the feature of tire images, and feature maps are obtained at the last layer. By replacing fully connected layers into convolution layers, final feature maps retain sufficient spatial information. By adding up-sampling layers, in the second phase, outputs with the same size as the original image can be generated. After the first two phases, we develop the coarse segmentation results and refine them through fusing multi-scale feature maps. The experimental results show that the proposed method can accurately locate and segment defects in tire images.