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Due to the limited computing resources and storage capacity of edge detection devices, efficient detection algorithms are typically required to meet real-time and accuracy requirements. Existing detectors often require a large number of parameters and high computational power to improve accuracy, which reduces detection speed and performance on low-power devices. To reduce computational load and enhance detection performance on edge devices, we propose a lightweight drone target detection algorithm, LD-YOLOv10. Firstly, we design a novel lightweight feature extraction structure called RGELAN, which utilizes re-parameterized convolutions and the newly designed Conv-Tiny as the computational structure to reduce the computational burden of feature extraction. The AIFI module was introduced, utilizing its multi-head attention mechanism to enhance the expression of semantic information. We construct the DR-PAN Neck structure, which obtains weak features of small targets with minimal computational load. Wise-IoU and EIoU are combined as new bounding box regression loss functions to adjust the competition between anchor boxes of different quality and the sensitivity of anchor box aspect ratios, providing a more intelligent gradient allocation strategy. Extensive experiments on the VisdroneDET-2021 and UAVDT datasets show that LD-YOLOv10 reduces the number of parameters by 62.4% while achieving a slight increase in accuracy and has a faster detection speed compared to other lightweight algorithms. When deployed on the low-power NVIDIA Jetson Orin Nano device, LD-YOLOv10 achieves a detection speed of 25 FPS.
Qiu et al. (Sat,) studied this question.