## 4 Experimental Results and Discussion
### YOLO-T Deep Learning Network Target Recognition  
The proposed YOLO-T network was trained on the GDXray-Expanded dataset. The detection accuracies and mAP values for all types of dangerous goods obtained by the proposed YOLO-T network are shown in Table 1. Table 1 also shows the highest detection accuracies of YOLO v2, YOLO v3, and YOLO v4 (Section 4.1). As can be seen, the detection accuracy of the proposed YOLO-T network for a single type of dangerous goods was greater than 95%, which is greatly improved compared with YOLO v3. This proves that using a transformer model as the backbone is better than the DarkNet-53 network in the original YOLO v3 when detecting grayscale images in the X-Ray security inspection task. From the perspective of comprehensive mAP indicators, the proposed YOLO-T network model demonstrates high competitiveness. The trained model was used to detect some one-to-one images corresponding to those in Section 4.2. The results are shown in Figure 13. As can be seen, no dangerous goods are not missed, and the location box is very close to the actual position of the objects. In addition, the partially occluded explosive GN in Figure 12(a),b was effectively detected. In addition, the severely occluded explosive GR in Figure 12(c) was well detected. As shown in Figure 12(d), the confusion of scissors was improved, and in Figure 12(f), the cross-placed scissors were also distinguished accurately. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Net-Name** & **GN** & **GH** & **GT** & **GR** & **Gun** & **Scissors** & **Knife** & **mAP** \\ \hline YOLO v2 & 0.9014 & 0.9039 & 0.9043 & 0.9091 & 0.9081 & 0.8707 & 0.9075 & 0.9007 \\ YOLO v3 & 0.7007 & 0.8008 & 0.7993 & 0.9083 & 0.9013 & 0.6895 & 0.8881 & 0.8126 \\ YOLO v4 & 0.8876 & 0.8994 & 0.9085 & 0.9221 & 0.9080 & 0.8472 & 0.9708 & 0.9062 \\ YOLO-T & 0.9741 & 0.9633 & 0.9841 & 0.9872 & 0.9880 & 0.9512 & 0.9891 & 0.9773 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of accuracy of different deep learning networks. Figure 13: Detection results obtained by proposed YOLO-T network model: (**a**) test image 1; (**b**) test image 2; (**c**) test image 3; (**d**) test image 4; (**e**) test image 5; (**f**) test image 6. Compared to the detection results of the YOLO network models, the detection accuracy of the proposed YOLO-T network was improved significantly relative to the detection of every type of dangerous good. This proves that the proposed YOLO-T network effectively addresses problems related to occlusion and multitarget recognition of X-ray security inspection images. The proposed network realizes accurate identification and target detection of dangerous goods in X-ray images. In addition, the transformer model used as the network backbone employs an attention mechanism rather than a convolution operation to extract features from the images, which effectively improves the detection performance for severely occluded and overlapping targets in X-ray images of dangerous goods. Here, we do not compare the training time of different types of models, because the training time is generally long and is affected by hardware devices. Compared with the training time, the detection time performance of these models is more important in actual detection. Therefore, we compared the detection time of different models for dangerous goods here. As shown in Figure 14, we tested the detection time of different methods under the same test dataset from Figure 13a-f. Through this experiment, we found that the average time spent by the YOLO v2 method to detect the target of dangerous goods is the shortest, followed by the YOLO-T method, and the YOLO v4 method takes the longest time. According to our previous discussion, the complexity of the YOLO v2 network is the lowest, and the relatively simple network structure saves a lot of time for the detection target. Nevertheless, the YOLO-T method proposed in this paper has the best performance and achieved competitive experimental results in the test of detection time.