Given the diverse prohibited varieties in current security inspection scenes and low-efficiency error-prone manual inspections, this study proposes network architecture, Res152-YOLO, based on the YOLOv4 optimized target detection network. Res152-YOLO uses the ResNet-152 network to replace the CSPDarknet-53 network in the original YOLOv4 and connects the improved ResNet to the YOLOv4 network to enhance the detection accuracy of dangerous goods in X-ray images. A series of networks such as YOLOv4 and Res152-YOLO are used to conduct comparative experiments on the same data set to compare the loss curves of the above-mentioned networks, the detection results for various dangerous goods, and the overall performance of each network. The results show that the improved network can improve the accuracy of security detection and eliminate potential safety hazards.