面向无人机边缘端的轻量化航拍小目标检测方法

徐鹤; 任治阳; 季一木; 李鹏

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面向无人机边缘端的轻量化航拍小目标检测方法

更新时间：2026-06-15

- 面向无人机边缘端的轻量化航拍小目标检测方法
- A lightweight method for small object detection in aerial imagery on UAV edge devices
- 物联网学报 2026年
- 作者机构：
  
  1.南京邮电大学计算机学院，江苏南京 210023
  2.南京邮电大学江苏省高性能计算与智能处理工程研究中心，江苏南京 210023
- 作者简介：
  
  季一木，jiym@njupt.edu.cn
- 基金信息：
  
  江苏省研究生科研实践创新计划项目(SJCX26)
- DOI：
  中图分类号： TP391.4
- 收稿：2026-05-17，
  
  修回：2026-06-15，
  
  录用：2026-06-15，
- 稿件说明：
移动端阅览
徐鹤, 任治阳, 季一木, 等. 面向无人机边缘端的轻量化航拍小目标检测方法[J/OL]. 物联网学报, 2026.

XU He, REN Zhiyang, JI Yimu, et al. A lightweight method for small object detection in aerial imagery on UAV edge devices[J/OL]. Chinese Journal on Internet of Things, 2026.
徐鹤, 任治阳, 季一木, 等. 面向无人机边缘端的轻量化航拍小目标检测方法[J/OL]. 物联网学报, 2026. DOI：

XU He, REN Zhiyang, JI Yimu, et al. A lightweight method for small object detection in aerial imagery on UAV edge devices[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

针对无人机航拍图像中小目标占比高、密集遮挡严重、浅层细节易随下采样衰减，以及机载边缘设备资源受限等问题，提出一种面向无人机边缘端的轻量化航拍小目标检测方法。该方法以 YOLO11s 为基线，将原始 P3/P4/P5 检测结构重构为 P2/P3/P4 结构，使高分辨率浅层特征直接参与微小目标预测；同时移除对无人机小目标场景收益有限的 P5 分支，并将高层语义建模模块前移至 P4 层，以降低冗余参数开销。进一步设计选择性引导模块、P2 引导的跨层再注入机制和自适应融合模块，以增强浅层细节表达并缓解多源特征融合冲突。实验结果表明，在 VisDrone 验证集上，该方法较 YOLO11s 基线模型的平均精度均值（mAP

mean average precision）@0.5和mAP@0.5:0.95分别提升 4.40 和 3.22 个百分点，参数量由 9.46M 降至 3.82M；在Jetson Orin NX 16 GB上采用TensorRT 32位浮点精度部署时，推理速度达到 52.63帧/s。结果表明，该方法在提升无人机小目标检测精度的同时，兼顾了模型规模和边缘端实时推理能力。

Abstract

To address the problems of high small-object proportion

severe dense occlusion

shallow-detail degradation during downsampling

and limited onboard edge resources in aerial images captured by unmanned aerial vehicles (UAVs)

a lightweight small-object detection method for UAV edge devices was proposed. YOLO11s was adopted as the baseline. The original P3/P4/P5 detection structure was reconstructed into a P2/P3/P4 structure

so that high-resolution shallow features could directly participate in tiny-object prediction. Meanwhile

the P5 branch with limited benefit for UAV small-object scenes was removed

and the high-level semantic modeling module was moved to the P4 layer to reduce redundant parameter overhead. A selective guidance block

a P2-guided cross-layer re-injection mechanism

and an adaptive fusion module were further designed to enhance shallow details and alleviate multi-source feature fusion conflicts. Experimental results on the VisDrone validation set showed that mAP (mean average precision)@0.5 and mAP@0.5:0.95 were improved by 4.40 and 3.22 percentage points

respectively

compared with the YOLO11s baseline

while the number of parameters was reduced from 9.46M to 3.82M. When deployed with TensorRT using 32-bit floating-point precision (FP32) on a Jetson Orin NX 16 GB platform

an inference speed of 52.63 frames per second (FPS) was achieved. The results indicate that the proposed method improves detection accuracy while maintaining a compact model size and real-time edge inference capability.

关键词

Keywords

references

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