Anomaly object detection in x-ray images with Gabor convolution and bigger discriminative RoI pooling

Abstract

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.