Detection and Identification Method of Dangerous Goods under X-ray Based on Improved YOLOv3

Abstract

X-ray safety inspection equipment is widely used in various public places for the detection of dangerous goods. At present, X-ray safety inspections mostly rely on manual inspections, so the detection efficiency is unsatisfactory. In the field of image detection technology, the deep learning based method has the advantages of low cost and simple configuration. In this paper ,we propose More Scales You Only Look Once version 3 (MS-YOLOv3) to detect and identify dangerous goods under X-ray.MS-YOLOv3 optimize the original You Only Look Once version 3 (YOLOv3) network structure by means of residual network and multi-scale fusion, improve the loss function and use the dangerous goods dataset under X-ray for training and testing. The experimental results show that the mAP of the optimized method is 7.08% higher than YOLOv3.