## 6 Conclusions and Future Work  
In this study, a novel, memory-efficient lightweight object-detection model specifically tailored for embedded UAV platforms in remote sensing is introduced, leveraging the FasterNet-16 backbone and optimized PConv convolution layers. The model significantly reduces memory bandwidth requirements compared to traditional deep separable convolutions, enabling more efficient computational resource use in embedded devices. Through rigorous testing on platforms like Nvidia Jetson Nano and Rockchip RK3566, the model  
Figure 12: Predictions on the Traffic-Net dataset across different traffic scenarios.  
demonstrated superior inference speeds, achieving a mean average precision (mAP) of 11.5% at 15.27 ms inference time for a 352 \(\times\) 352 resolution on RK3566, significantly outperforming contemporary models like FastestDet and YOLOv88. This breakthrough underscores the model's capability to balance computational efficiency and detection precision effectively, setting a new standard for real-time object detection in UAV transportation applications.  
In future research, the focus will be on enhancing the model's performance by addressing the trade-off between accuracy and inference time. This will involve exploring advanced network architectures to achieve better speed and precision, integrating techniques such as hyperparameter tuning and attention mechanisms, and improving robustness across diverse environmental conditions. Additionally, plans will include gathering user feedback from UAV operators to identify practical challenges and areas for improvement in the model's usability. In UAV traffic management and urban monitoring, drones must transmit detection results back to communication base stations, making UAV communication a critical area for future research. Ensuring secure communication channels is vital, especially in scenarios involving real-time data transmission and decision-making. The integrity, confidentiality, and availability of communication links between the UAV and its control center must be maintained to prevent malicious attacks such as data interception, spoofing, or jamming [52; 53].  
Conceptualization, Z.L. and C.C.; methodology, Z.L.; validation, C.C., Z.H. and Y.C.C.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., C.C., Z.H., Y.C.C., L.L. and Q.P. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (62072360, 62172438), the key research and development plan of Shaanxi province (2021 ZDLGY02-09, 2023-GHZD-44, 2023-ZDLGY-54), the National Key Laboratory Foundation (2023-JCJQ-LB-007), the Natural Science Foundation of Guangdong Province of China (2022A1515010988), Key Project on Artificial Intelligence of Xi'an Science and Technology Plan (23 ZDCYJSGG0021-2022, 23 ZDCYYYJCJ008, 23 ZDCYJSGG0002-2023), Xidian-UTAR China Malaysia Science and Technology Institute-the Fundamental Research Funds for the Central Universities/XURF-2024-QTZX24089, and the Proof-of-concept fund from Hangzhou Research Institute of Xidian University (GNYZ2023 QC0201, GNYZ2024 QC004, GNYZ2024 QC015).  
The data can be provided to any interested reader upon request as usual practice.  
The authors declare no conflicts of interest.