Aiming at the problems of low efficiency and accuracy in detecting internal defects of automobile wheels under traditional methods,and the accuracy is not up to industry standards,this paper proposes a method for segmentation of image defects in X-ray images of wheels based on improved U-Net neural network,AW-Net.This method cascades two U-shaped networks to extract image features in a three-level jump connection mode; at the same time,the attention mechanism is integrated in the jump connection process to solve the problem that the change of small targets is easy to be missed,and passes Experiments verify that a combination of multiple activation functions is used to achieve more accurate semantic segmentation of X-ray images of the hub, increase the fitting ability of the network, and improve the robustness of the network.The experimental results show that the improved algorithm has a false detection rate of 2.73%, a leakage rate of 0 and a recognition rate of more than 93% for the internal defects of automotive wheels in the data set constructed in this paper, and its segmentation accuracy is higher than that of traditional image segmentation networks, such as fully convolutional network (FCN) and U-Net, and the edge segmentation of this method is flatter and meets the needs of nondestructive detection of internal defects of modern wheels.