## 1 Introduction  
Unmanned systems (US), including unmanned ground vehicles (UGVs) and UAVs, are increasingly crucial for driving technological advancements within the transportation sector [1; 2; 3]. UAVs, providing a bird's eye view, enhance traditional traffic monitoring methods by collecting real-time data on vehicle speed, distance, and location, thereby supporting efficient traffic management and collision avoidance [4]. Utilizing remote-sensing technologies, UAVs significantly contribute to congestion analysis, vehicle tracking, and safety evaluations in urban traffic systems [5; 6]. Equipped with high-resolution cameras and advanced image-processing algorithms, UAVs dynamically monitor and analyze traffic flow, promptly identify bottlenecks, track vehicle trajectories, and assess accident scenes and driver behavior. These capabilities substantially improve traffic management efficiency and road safety [7; 8]. For most UAV applications, precise target detection and advanced image-processing algorithms are essential. Equipped with sensors and refined detection algorithms, UAVs can effectively perceive their surroundings, accurately identify targets, and make corresponding decisions [9]. Numerous studies are currently focused on developing lightweight target-detection algorithms tailored for UAV platforms. Notably, improvements to the YOLO series have led to several new approaches, such as [10; 11; 12]. These studies underscore the diversity and critical importance of UAV target-detection technologies. However, most research is conducted on consumer-grade GPUs, like the Nvidia RTX 3060, primarily focusing on improving detection precision without testing on actual UAV computing platforms. This type of unreal-time detection overlooks the specific computational and resource constraints of UAV platforms. A thorough assessment of these target-detection algorithms' real-world performance and potential applications can only be achieved through stringent testing and validation on UAV-embedded devices [13]. Balancing the computation resources, detection capabilities, and processing speed is a critical technological issue in UAV application development [14]. Real-time UAV target detection typically relies on embedded devices, which not only serve as the core computing platforms within the UAV systems but also operate under severe payload and power supply constraints. These constraints are essential to ensure prolonged UAV operations and the completion of numerous tasks [4]. Consequently, these devices are required to process significant data volumes in real time while adhering to strict power and size limitations. However, the limited computational capacity and memory resources of embedded devices present considerable challenges for deploying efficient target-detection models [7]. In recent years, comprehensive research has been dedicated to enhancing multi-object-detection capabilities and optimizing inference speeds. Studies such as [15; 16] have introduced lightweight target-detection algorithms designed for edge computing platforms. Additionally, research like [9] emphasizes the critical importance of real-time data processing, which is crucial for operational environments requiring rapid decisions. Although there is a noticeable discrepancy in detection precision between embedded devices and high-performance consumer-grade GPUs, the relevance of these algorithms in practical scenarios should not be underestimated. However, current research, such as [15; 16], primarily utilizes high-performance artificial-intelligence-embedded platforms like the Nvidia Jetson Nano for experimental validation. While these platforms excel in detection precision, their high cost and power consumption may not be economical for UAV applications. Specifically, the Jetson Nano costs up to 1299 RMB and consumes 10 watts of power. Such high power consumption is particularly detrimental in UAV applications, as it directly affects the drone's flight endurance.