## 1 Introduction
High power demands mean UAVs need to carry larger, heavier batteries, increasing costs and potentially reducing payload capacity and flight duration. Figure 1 showcases the diverse applications of UAVs in remote sensing, and the critical specifications for processor modules in UAVs include capabilities for real-time inference, lightweight architecture, minimal power usage, and cost efficiency [13]. Figure 1: Illustration of UAV remote sensing applications in transportation systems and the demands for processor module [7; 13]. Therefore, exploring more cost-effective and energy-efficient alternative platforms is essential. Extensive research has been undertaken on lightweight target-detection models tailored for cost-effective embedded platforms. Importantly, a variety of state-of-the-art lightweight object-detection models, including YOLO-Fastest v1.1, YOLO-Fastest V2, and FastestDet, have been evaluated [17]. These models have been tested on Rockchip-based embedded platforms, such as RK3566 and RK3568, where they demonstrated robust performance and notable potential for real-time applications. The aforementioned models must maintain a parameter size under 0.5 MB for real-time inference on Rockchip-based platforms. In comparison, the YOLOv8s model suitable for the Jetson Nano features a significantly larger parameter count of 11.2 MB. However, as these methods are not originally designed for UAV-specific applications, the challenge still exists:  
* The limited memory of embedded devices leads to frequent memory accesses and data transfers, considerably slowing down the inference process and impacting the real-time perceptual capabilities of UAV systems. Current models lack optimizations for small-memory hardware constraints. Addressing this issue, this study introduces a memory-efficient, lightweight target-detection model designed specifically for low-cost and memory-constrained embedded platforms. The contribution includes the following:
* An in-depth analysis of the FasterNet backbone network is conducted, resulting in the reduction of feature-processing dimensionality and the introduction of the FasterNet-16 backbone network. This enhancement enables faster and more efficient feature extraction in target detection. * Multi-scale feature fusion in the neck module is evaluated, and a strategy utilizing multiple PConv convolutions is developed. This approach ensures optimal feature utilization while maintaining a minimal structural footprint within the target-detection network. * Based on various datasets and three different embedded platforms, extensive experiments are conducted. The proposed model demonstrates improvements in both detection precision and inference speed on the Rockchip RK3566-embedded platform compared to state-of-the-art methods. The structure of this paper is organized as follows: Section 2 reviews related work and recent developments in UAV detection systems; Section 3 elaborates on the proposed lightweight object-detection algorithm, detailing the design of the FasterNet-16 backbone and the multi-scale feature-fusion neck module; Section 4 outlines the experimental setup and discusses the results obtained on diverse embedded platforms; Section 5 analyzes these results, highlights the limitations of the current study, and proposes directions for future research; Section 6 concludes the paper.