# Adaptive Feature Fusion for Small Object Detection
## 4 Analysis of Experimental Results
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **Model** & **Backbone** & **Input Resolution** & **mAP/\%** & **Paras/MB** \\ \hline \  
Table 8 shows a comparison of the various class accuracies of mainstream networks and our algorithm. From Table 5, it can be seen that the AP of each category of Bi-YOLOX was higher than other one-stage classical network algorithms, and the car category with the highest AP reached 77.7%, which was 8.3% higher than the basic network and 32.2% higher than that of RetinaNet. Compared with the prototype network, the bus category was improved by up to 12%, 40.8% higher than RetinaNet, and the pedestrian class with high missed detection rate and false detection rate was increased by as much as 14.97%. For the bicycle category with a smaller object size, AP increased by 8%. Compared with TPH-YOLOv5, the accuracy of MMF-YOLO in the three categories of bus, van, and awning-tricycle was slightly inferior, but the difference between the two was not large. This is because the resolution of its input image was much higher than that of the input image of the MMF-YOLO network. If the size of the input image is the same, the MMF-YOLO network is likely to overtake TPH-YOLOv5. Compared with the base network, the APs of each class of our network had different degrees of improvement and were higher than their average accuracy. In summary, the improved algorithm in this paper has significant advantages over other algorithms in terms of the average detection accuracy or category accuracy or model size. Figure 9 visually shows the accuracy comparison of each category between the classical network and the MMF-YOLO network. It is clear from Figure 9 that our network occupied a high position in most categories and was higher than every category in the original network. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Model** & **Paras/MB** & **FLOPs** \\ \hline  & YOLOv5-L & 178.09 & 114.413 G \\  & Faster R-CNN & 522.21 & 402.159 G \\  & RetinaNet & 36.53 & 166.711 G \\  & YOLOv3 & 234.9 & 155.249 G \\  & SSD & 95.17 & 277.8 G \\  & YOLOv4 & 244.11 & 26.657 G \\  & MMF-YOLO & 29.33 & 63.562 G \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the model parameters and computational amount. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Car** & **Bus** & **Truck** & **Van** & **Pedestrian** & **Motor** & **People** & **Tricycle** & **Awn-Tri** & **Bicycle** \\  & **7\%** & **7\%** & _1\%_ & _1\%_ & _1\%_ & _1\%_ & _1\%_ & _1\%_ & _1\%_ & _1\%_ \\ \hline TPH-YOLOv5 [9] & 68.9 & 61.8 & 45.2 & 49.8 & 29.0 & 30.9 & 16.8 & 27.3 & 24.7 & 15.7 \\ Faster R-CNN [42] & 51.7 & 31.4 & 19.0 & 29.5 & 21.4 & 20.7 & 15.6 & 13.1 & 7.7 & 6.7 \\ Cas R-CNN [42], * & 54.6 & 34.9 & 21.6 & 31.5 & 22.2 & 21.4 & 14.8 & 14.8 & 8.6 & 7.6 \\ RetinaNet [42] & 45.5 & 17.8 & 11.5 & 19.9 & 13.0 & 11.8 & 7.9 & 6.3 & 4.2 & 1.4 \\ YOLOv4 [44] & 64.3 & 44.3 & 22.7 & 22.4 & 24.8 & 21.7 & 12.6 & 11.4 & 7.6 & 8.6 \\ YOLOv3-LITE [45] & 70.8 & 40.9 & 21.9 & 31.3 & 34.5 & 32.7 & 23.4 & 15.3 & 6.2 & 7.9 \\ YOLOX & 69.4 & 46.6 & 42.3 & 40.2 & 33.0 & 34.7 & 22.1 & 22.5 & 12.4 & 11.3 \\ MMF-YOLO & 77.7 & 58.6 & 54.19 & 49.12 & 47.97 & 40.4 & 30.4 & 29.4 & 15.39 & 19.2 \\ \hline \hline \end{tabular}  
* Cas R-CNN is Cascade R-CNN.  
\end{table} 
Table 8: Comparison of various types of AP between the mainstream algorithms and the MMF-YOLO algorithm.