# Adaptive Feature Fusion for Small Object Detection
## 4 Analysis of Experimental Results
### Comparative Experiments  
In order to verify the reliability of the proposed network, we roughly divided the training into three stages, where each stage had 40 epochs, and randomly selected one epoch in each stage to evaluate the baseline network YOLOX and MMF-YOLO, the results of which are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{3}{c}{**MMF-Net**} & \multirow{2}{*}{**M-CBAM**} & \multirow{2}{*}{**Head**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**Paras/MB**} \\ \cline{2-2} \cline{6-7}  & & \(+\alpha\) & \(-\alpha\) & & & & 35.73 & 38.12 \\ \hline \multirow{2}{*}{1} & \(\surd\) & \(\surd\) & & & & 35.32 & 38.12 \\  & \(\surd\) & & \(\surd\) & & 41.21 & 29.3 \\  & \(\surd\) & & & \(\surd\) & 38.01 & 29.3 \\  & & & & \(\surd\) & & 33.53 & 34.27 \\  & & & & \(\surd\) & \(\surd\) & 39.94 & 27.75 \\  & & & & & \(\surd\) & 36.51 & 27.67 \\  & & \(\surd\) & \(\surd\) & \(\surd\) & 41.69 & 29.37 \\  & \(\surd\) & & \(\surd\) & \(\surd\) & 42.23 & 29.33 \\  & & & & & & 33.10 & 34.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results. We select the 40 th epoch, the 80 th epoch, and the 120 th epoch for evaluation in the three stages. As can be seen in Table 5, the benchmark and MMF-YOLO networks had low mAPs in the 40 th epoch experiment, but our network was slightly better, at 2.11% higher than YOLOX. At the 80 th epoch, after sufficient training, the network began to converge. The mAP of YOLOX and MMF-YOLO reached a high level, while the mAP of MMF-YOLO was higher, reaching 40.35%, which was 8.51% higher than that of YOLOX. At the 120 th epoch, the network was relatively stable and had completely converged. The accuracy of the two networks was close to the best level, reaching 33.2% and 41.45%, respectively. Our network showed better performance than the benchmark network at different stages. Although the performance in the first stage was poor, the overall level was higher, which proves that our network can effectively improve the accuracy of small object detection and proves the reliability of the network. In order to further verify the effectiveness of the proposed algorithm for small object detection, we experimented with the MMF-YOLO algorithm and other mainstream algorithms for object detection on the VisDrone dataset, and set the input image size to 640 \(\times\) 640. Due to the limitation of the experimental equipment, some experimental data were obtained by referring to the literature. The experimental results are shown in Table 6.  
It can be seen from Table 6 that our network MMF-YOLO performed better on mAP, up to 42.23%, and its input resolution was only 640 \(\times\) 640, and the parameter size was only 29.33 MB, which was 4.74 MB less than the original network. Compared with other networks in the YOLO series, YOLOv5, YOLOv3, YOLOv4, the mAP increased by about 14%, 20%, and 12%, respectively, which may be further improved if the input resolution is the same. Compared with the TPH-YOLOv5 network, the mAP increased by 3.05%, and it used YOLOv5-L as the backbone network, while our network used the depth and width of YOLOX-s, so its parameters were much larger than the improved network. Compared with other mainstream algorithms with different input resolutions, the mAP also had different improvements. Compared with the RetinaNet algorithm, the mAP had been improved by 28.33%. Table 7 shows the model parameter size and model calculation amount of the mainstream deep learning algorithms. From Table 8, it can be seen that MMF-YOLO had the least model parameters, only 29.33 MB, which was 5.22 MB less than the benchmark network, sacrificing some computational complexity. The complexity increased to 63.5 G, which may be the amount of computation brought by the process of increasing multiple fusion paths.