Anchor-Free Weapon Detection for X-Ray Baggage Security Images

Abstract

Considering the real-time and high-precision requirements of image processing in X-ray baggage security screening; and problems such as the inflexibility and complex computation of anchor-based object detection, this paper introduces an anchor-free mode convolutional neural network object detection method for detecting weapons (knives and handguns) in X-ray baggage security images. The advantage of the anchor-free method over the anchor-based method is that the size of the anchor box does not have to be set, and the generalization ability is strong; the absence of the anchor box reduces the number of computations, and solves the problem of unbalanced positive and negative samples in the anchor-based method. To fully evaluate the effectiveness of the anchor-free method for X-ray baggage screening image detection, a large number of images containing knives and handguns were collected and annotated in the early stages of this work to produce a dataset that could be used for training. Six mainstream anchor-free methods (CornerNet, CenterNet, CornerNet-Lite, ExtremeNet, Objects as Points and You Only Look Once(YOLOx)) are introduced. For experimental integrity, this paper adds an anchor-based comparison experiment, using Faster-RCNN, YOLOv3 and YOLOv5 to perform the same work. The experimental results show that the YOLOx, Objects as Points and ExtremeNet anchor-free methods used in this paper have excellent performance in weapon detection in X-ray baggage security images. Among them, the mean average precision (mAP) of YOLOx combined with the CSPDarknet53 network reached 0.905, and the mAP of ExtremeNet combined with the Hourglass-104 network reached 0.900; the performance of the Objects as Points method was also good. All these methods performed better than the anchor-based methods compared in this paper. Therefore, we believe that the anchor-free method has a practical effect in weapon detection for X-ray luggage images.