# A Efficient and Accurate UAV Detection Method Based on YOLOv5s  
[PERSON]  
1 State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology,  
Hefei 230037, China; wangtongz1@outlook. flight status and operator details from intercepted signals; however, it cannot detect drones without wireless signals and faces challenges with signal interference in real-world settings. To meet the demands of the high-precision detection of low, slow, and small unmanned aerial vehicles such as drones and cruise missiles in modern warfare, multi-modal, multi-perspective networked optoelectronic reconnaissance technology has become essential [8; 9; 10; 11]. These aerial vehicles, characterized by minimal radar signatures, high maneuverability, and unpredictable flight paths, pose a significant threat to national and societal security. In this context, efficient target detection is crucial yet it is challenged by limited computational resources [12]. Currently, there is a relative scarcity of algorithms specifically designed for drone-based object detection. Developing specialized object detection algorithms for drones is an important and yet underexplored area of research. [PERSON] introduces a novel lightweight network structure named Cross-Stage Partially Deformable Network (CSPDNet) [13]. The core approach of CSPDNet involves the use of a Deformable Separable Convolution Block for feature separation, significantly reducing the computational load of convolutions and enhancing the information exchange capability through adaptive sampling of the separated feature map. Additionally, a channel weighting module is introduced to compensate for the effects of point-wise convolutions and filter out more significant feature information. While CSPDNet achieves a certain balance between model parameter size and detection accuracy, shortcomings include potential real-time performance issues and the detection efficacy for small targets, which require further exploration in future studies. [PERSON] introduces a strategy for the precise detection and localization of unmanned aerial vehicle swarms [14]. The primary approach involves the use of a coprime array combined with the KT-Dechirp and FS-RAM techniques for super-resolution detection and localization. This strategy capitalizes on the large array aperture of the coprime array and the integration of coherent long-time integration with gridless sparse techniques to enhance the signal-to-noise ratio and resolution of detection. However, the drawbacks include sensitivity to low signal-to-noise ratios and high processing complexity, necessitating further optimization to enhance reliability and efficiency in practical applications. [PERSON] [15] introduces a new method for small object detection in UAV images, termed Feature Fusion and Scaling-based Single Shot Detector, complemented by spatial context analysis. This method enhances feature representation through an improved feature fusion module and an added deconvolution module, thereby increasing the detection accuracy for small objects. Additionally, the paper proposes spatial context analysis by recalculating the reliability of detection results through intra-class and inter-class distances among different object instances. However, the shortcomings of this method include relatively slower processing speed and a need for further enhancement in detecting extremely small or occluded objects. When multiple detection devices operate in coordination within a network, achieving optimal detection accuracy with constrained computational resources is imperative. This necessitates not only high precision in target recognition models but also optimization in terms of model parameter size, computational load, and processing speed. For base station processors with limited computing power, low energy consumption, and strict constraints on memory and data bandwidth, lightweight design and model compression acceleration have become particularly important [16].