## 4 Experimental Results
### Methods Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP** & **AP50** & **Input Resolution** & **Inference Time** & **Model Parameters** \\ \hline yolov8s & 17.3\% & 31.00\% & 640 \(\times\) 640 & 140.72 ms & 11.2 M \\ yolo-fastestv2 & 10.1\% & 20.05\% & 320 \(\times\) 320 & 19.86 ms & 0.25 M \\ FastestDet & 10.3\% & 21.00\% & 352 \(\times\) 352 & 20.47 ms & 0.24 M \\
**Proposed model** & **10.8\%** & **21.50\%** & **320 \(\times\) 320** & **12.13 ms** & **0.4035 M** \\
**Proposed model** & **11.5\%** & **23.05\%** & **352 \(\times\) 352** & **15.27 ms** & **0.4035 M** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed model with object-detection models on RK3566. RK3566 and 23.75% for the H618. Figure 9 distinctly illustrates these performance benefits, showing the proposed model's higher frame rates across all tested platforms. Such results underscore the proposed model's optimized processing efficiency, crucial for real-time object-detection tasks. Further evaluations are conducted to assess inference speeds across different computational core configurations on the embedded platforms, as shown in Table 6. Given that computing modules in UAV operations typically handle multiple tasks simultaneously--such as flight control and battery management--the allocation of computational resources is pivotal. The proposed model exhibits exemplary inference speeds, surpassing those of FastestDet across all processor configurations. Notably, on platforms like the Nvidia Jetson Nano and Rockchip RK3566, the proposed model maintains consistent performance, achieving inference times of at least 24.78 ms, which correlates to approximately 50 FPS, thus fulfilling the requirements for real-time processing. On the AllWinner H618 platform, the model demonstrates robust performance even at the lowest configuration, maintaining an inference time of approximately 56.32 ms, or nearly 18 FPS, indicative of strong real-time processing capability on resource-limited devices. Additionally, the performance of the model is analyzed under varying memory constraints, highlighting its efficiency with a reduced parameter count. The inference times across platforms--from the Jetson Nano to the Rockchip RK3566, and the AllWinner H618--show the proposed model's adaptability to different memory bandwidths. Specifically, the proposed model records inference times of 17.23 ms, 15.27 ms, and 30.41 ms, respectively, at a resolution of 352 \(\times\) 352 on a quad-core setup, performing better than FastestDet under similar conditions. This demonstrates lower performance degradation and emphasizes the model's capability to sustain high efficiency in environments with constrained memory bandwidth. Figure 10 presents visualization samples from the proposed model (352 \(\times\) 352) applied to the VisDrone dataset, illustrating enhanced detection precision during daytime compared to nighttime due to better lighting conditions, superior performance in detecting larger objects like vehicles over smaller ones such as pedestrians, and challenges in handling scenarios with occlusion, where the detection precision declines as objects overlap or obscure each other. These observations underscore the need for further enhancements in low-light performance and occlusion management to improve the overall robustness of the model.