## 4 Experiments
### Contrast Experiments  
The MFFCESSC-SSD is compared with other conventional detectors on VisDrone2019 to further verify the performance of the proposed network, and the results are shown in Table 2. Earlier studies based on VisDrone2019 focused on improving accuracy, including Cascade region-based convolutional neural networks (Cascade R-CNN) [36], CenterNet [36], Synergistic Network (SyNet) [37], and Transformer Prediction Head YOLOv5 (TPH-YOLOv5) [38]. Among them, SyNet is a synergistic architecture that combines Cascade R-CNN and CenterNet using a weighted box. Therefore, SyNet obtains superior results in terms of mAP. YOLO with CSPDarknet as its backbone is widely used and proven to be effective for UAV image object detection. The methods' accuracy increases by 31% and they achieve 16 FPSs with an image size of 416 \(\times\) 416, which is still below the threshold of 30 FPSs for real-time detection. Notably, model performance is improved with the increase in input size. CESAC and the Self-Attention Guidance and Multi-Scale Feature Fusion-Based Network (SGMFNet) obtain a detection accuracy of 31.7% with high-resolution images. However, the inference speed is still insufficient to meet the requirements of real-time processing. The visualization of the detection results of each algorithm on the VisDrone2019 dataset are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Base Detector** & **Image Size** & **Backbone** & **mAP (\%)** & **FPS/s** \\ \hline
**Cascade R-CNN**[36] & R-CNN & 960 \(\times\) 540 & ResNet-50 & 24.7 & - \\
**CenterNet**[36] & CornerNet & 512 \(\times\) 512 & Hourglass-104 & 14.3 & - \\
**SyNet**[37] & R-CNN & 960 \(\times\) 540 & ResNet-50 & 26.2 & - \\
**TPH-YOLOv5**[38] & YOLOv5 & 1536 \(\times\) 1152 & CSPDarknet53 & 31.0 & - \\
**Improved YOLOv4**[39] & YOLOv4 & 416 \(\times\) 416 & CSPDarknet & 27.0 & 16 \\
**CEASC**[34] & GFL V1 & 1333 \(\times\) 800 & ResNet18 & 28.7 & 22 \\
**SGMFNet**[40] & - & 1536 \(\times\) 1536 & CSPDarkNet53 & **31.7** & 23 \\
**SSD** & - & 960 \(\times\) 540 & ResNet-50 & 19.9 & 55 \\
**MFFCESSC-SSD (ours)** & SSD & 960 \(\times\) 540 & ResNet-50 & **33.1** & **56** \\ \hline \hline \end{tabular}  
* Cascade R-CNN, CenterNet, SyNet, TPH-YOLOv5, Improved YOLOv4, CEASC, and SGMFNet are all influential algorithms in the field of UAV image recognition proposed in recent years.  
\end{table}
Table 2: Comparison results of different algorithms using VisDrone2019.  
Figure 4: Visualization of detection results. Yellow ovals highlight small objects in validation set, which cannot be detected by SSD. By adding MFF to SSD, model generated denser detection boxes on VisDrone2019 and successfully detected small objects in two datasets.  
The SSD with ResNet as its backbone is faster than the above object detection algorithms, but its mAP is only 19.9%. The MFFCESSC-SSD outperforms the conventional SSD by 13.2% in terms of mAP while having limited parameters. The MFFCESSC-SSD is 1.4% more accurate than SGMFNet and runs 1.41 times faster. The contrast experiments reveal that the MFFCESSC-SSD is a competitive algorithm in terms of accuracy and inference speed.