## 4 Performance Analysis
### Experimental Verification and Result Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **Train Data** & **Test Data** & \(mAP\)**(\%) & \(AP_{\mathbf{50}}\) & \(AP_{\mathbf{75}}\) & \(APS\) & FPS & **Parameters (M)** \\ \hline SSD(300) & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 43.2 & 49.1 & 44.5 & 41.3 & 40 & - \\ \hline RCNN & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 59.3 & 60.2 & 55.1 & 43.2 & 37 & - \\ \hline Faster RCNN & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 64.4 & 68.2 & 64.1 & 46.7 & 49 & 31.5 \\ \hline YOLOv3-Tiny & COCO2017\_trainval +TUM\_test & COCO2017\_test-dev +TUM\_test & 55.3 & 63.7 & 58.1 & 52.2 & 74 & 8.7 \\ \hline Ours (v3) & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 60.4 & 67.2 & 64.9 & 60.3 & 65 & 12 \\ \hline YOLOv4-Tiny & COCO2017\_trainval +TUM\_test & COCO2017\_test-dev +TUM\_test & 59.4 & 65.3 & 60.1 & 58.6 & 85 & 27.6 \\ \hline Ours (v4) & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & **64.5** & **68.8** & **65.7** & **65.6** & 81 & 31 \\ \hline YOLOv5s & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 37.0 & 56.8 & 37.4 & 35.0 & 416 & 7.2 \\ \hline YOLOv5m & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 44.3 & 64.1 & 45.4 & 40.0 & 294 & 21.2 \\ \hline YOLOv5l & COCO2017\_trainval +TUM\_train & COCO2017\_test-dev +TUM\_test & 47.7 & 67.3 & 49.0 & 47.2 & 227 & 46.2 \\ \hline YOLOv5x & COCO2017\_trainval +TUM\_test & COCO2017\_test-dev +TUM\_test & 50.8 & 68.9 & 50.7 & 47.3 & 144 & 86.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AP values and speed of different networks on COCO+TUM dataset. Figure 7: (**a–h**) shows some detection results of the MS COCO2017 dataset with our method. As illustrated in Table 5, adding VaryBlock 1-3 to the basic network improves detection accuracy from 55.3 to 57.6. The number of convolution cores in the convolution layer will increase as the number of layers increases, since the first seven layers of YOLOv3-Tiny are the most significant convolution layers. As the number of convolution kernels grows, the amount of computations during detection grows, resulting in a fall in FPS from 74 to 70 frames. When utilizing convolution kernels of the same size, the more convolution kernels used, the more features are extracted. As a result, more features may be recovered after adding VaryBlocks 4-6 to the convolution layer. From 57.6% to 61.5%, the accuracy has improved. The FPS is only nine frames lower than the original model, yet it still passes the real-time requirements. Table 6 is the test results for WGAN. From the comparison results, it can be seen that APS and APM increased by 2.1% and 1.9%, respectively, in the detection results of YOLOv3-Tiny combined with WGAN, and the detection indexes for large objects basically remained unchanged. Generally speaking, the network using WGAN will obtain a higher value in the small object detection index, which means that the expansion of data by WGAN can make the network model more accurate in small object detection. Table 7 displays the experimental outcomes. The results show that whether utilizing YOLOv3-Tiny or the upgraded YOLOv3-Tiny, the network detection time with k-means++ is faster than with the normal k-means network, and the AP also has a higher value. Figure 8 shows the results of YOLOv3-Tiny and the improved YOLO-Tiny (based on YOLOv3-Tiny) object detection algorithm on the TUM dataset, respectively. As can be seen from the comparison between Figures 8a,b, there is a difference in the detection results of the person in a book. Figure 8b detects a person in a book while Figure 8a detects nothing in the book. It means that our model has good performance for small object detection. Figures 8c,d both detect laptop, keyboard, and mouse, but books are not detected in the YOLOv3-Tiny model. The experimental comparison proves that the model we trained is more suitable for the TUM dataset and has higher accuracy. Figures 8e,f have different detection results for cars.