## 4 Performance Analysis
### Experimental Verification and Result Analysis
Figure 8f has detected two cars, while Figure 8e has detected only one cars. The tape on the far right of the table is mistakenly detected as a bowl, which is corrected in our model as shown in Figure 8f. The deeper the network, the smaller the feature maps that are retrieved \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network Structure** & **FPS** & **mAP(\%)** & **APS (\%)** & **APM (\%)** & **APL (\%)** \\ \hline YOLOv3-Tiny & **74** & 55.3 & 52.2 & 57.2 & 60.3 \\ YOLOv3-Tiny+WGAN & **74** & 57.3 & 55.7 & 59.1 & **60.4** \\ Our method & 65 & **60.4** & **57.1** & **60.8** & 60.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The test results with and without WGAN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Structure** & **FPS** & **mAP (\%)** & **APS (\%)** \\ \hline YOLOv3-Tiny & **74** & 55.3 & 52.2 \\ YOLOv3-Tiny + VaryBlock 1-3 & 70 & 57.6 & 55.2 \\ YOLOv3-Tiny+ VaryBlock 1-6 & 68 & **61.5** & **57.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The test results with different layers add VaryBlock. by the convolution and pooling processes of convolutional neural networks, making it difficult to completely describe the features of small objects [58]. Small items, such as a cup in the distance and a dog in a magazine, are better detected in our model, as seen in the comparison between Figures 8g,h. It demonstrates that the model developed in this research has a good small-object detection effect. The experimental results of Table 8 show that compared with other object detection methods, the proposed method has obtained relatively considerable mAP. The mAP increased dramatically with smaller things in particular. It shows that our approach has excellent performance. Compared with baseline YOLOv3-Tiny, the proposed algorithm-based YOLOv3-Tiny improves AP values in eight categories, including keyboard (45.5 to 57.2), cans (52.1 to 55.3), plant (56.5 to 59.6), mouse (56.1 to 58.8), TV monitor (42.1 to 54.5), cup (51.7 to 68.6), person (62.1 to 64.1) and laptop (49 to 56.5). Each class has its own set of improvements, which can be used to test the model's resilience. At the same time, YOLOv4-Tiny benefits from the same improving impact. In addition, the approach we proposed is slightly less performing in both categories than in the baseline. This could be due to a number of things. The most plausible reason is that GAN generates random images, and different noises can result in somewhat different experimental findings. The detection approach suggested in this research, on the other hand, is slower than the original YOLO-Tiny because of the increased number of network layers, but it still meets the real-time requirements. We will continue to investigate this matter in the future. Figure 9 shows the YOLOv3-Tiny loss curve as well as the improved technique. Our method's loss value declines slower than YOLOv3-Tiny, as shown in the graph. Due to the addition of the VaryBlock module, the increase in the number of network layers reduces the information loss caused by the pooling layer, and the convergence time will be relatively extended. All the data show that our method is stable and convergent, the final loss value is close to YOLOv3-Tiny, and the detection accuracy is greatly improved, although some detection speed is sacrificed. Figure 8: Detection results of TUM dataset using YOLOv3-Tiny and our method (v3). (**a,c,e,g**) show results of YOLOv3-Tiny; (**b,d,f,h**) shows results of our method (v3).