In airports, railway stations and other public places, security inspectors generally use the way of viewing x-ray images for security inspection, so false detection and missed detection often occur. In this paper, an automatic anomaly object detection method in x-ray images is proposed under a two-stage framework. At the first stage, a learnable Gabor convolution layer is introduced into ResNeXt to facilitate the network to capture the edge information of objects. Then, region proposal network (RPN) is used to determine the candidate regions of objects as well as perform coarse classification. At the second stage, bigger discriminative RoI pooling (BDRP) is proposed to classify the candidate boxes to improve the classification accuracy of objects. Furthermore, dense local regression (DLR) is applied to predict the offset of multiple dense boxes in region proposals to locate the objects accurately. Experimental results on the SIXray and OPIXray datasets show that, compared with the state-of-the-art methods, the proposed method can achieve a competitive detection performance.
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.
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