Abnormal Object Detection in X-ray Images with Self-normalizing Channel Attention and Efficient Data Augmentation

In this paper, an abnormal object detection method in X-ray images is proposed under the framework of YOLO. ResNeXt-50 is adopted as the backbone network to extract the deep features. And a Self-normalizing Channel Attention Mechanism (SCAM) is proposed and introduced into the high layer of ResNeXt-50 to enhance the semantic representative ability of the features. According to the characteristics of X-ray images, an efficient data augmentation method is also proposed to enlarge the amount of the training data samples, which facilitates to improve the training performance of the network. The experimental results on the public SIX-ray and OPIX-ray datasets show that, compared with the methods of YOLO series, the proposed method can obtain a higher detection accuracy.