X-ray Security Inspection Image Dangerous Goods Detection Algorithm Based on Improved YOLOv4

Abstract

Aiming at the problems of multi-scale and serious overlap of dangerous goods in X-ray security-inspection-image samples, an X-ray dangerous-goods-detection algorithm with high detection accuracy is designed based on the improvement of YOLOv4. Using deformable convolution to redesign YOLOv4’s path-aggregation-network (PANet) module, deformable convolution can flexibly change its receptive field based on the shape of the detected object. When the high-level information and low-level information are fused in the PANet module, deformable convolution is used to align features, which can effectively improve the detection accuracy. Then, the Focal-EIOU loss function is introduced, which can solve the problem of the CIOU loss function being prone to causing severe loss-value oscillation when dealing with low-quality samples. During training, the network can converge more quickly and the detection accuracy can be slightly improved. Finally, Soft-NMS was used to improve the non-maximum suppression of YOLOv4, effectively solving the problem of the high overlap rate of hazardous materials in the X-ray security-inspection dataset and improving accuracy. On the SIXRay dataset, this model detected 95.73%, 83.00%, 82.95%, 85.13%, and 80.74% AP for guns, knives, wrenches, pliers, and scissors, respectively, and the detected mAP reached 85.51%. The proposed model can effectively reduce the false-detection rate of dangerous goods in X-ray security images and improve the detection ability of small targets.