A Multi-Channel Lightweight Small Object Detection Algorithm for Low-Altitude Reconnaissance Scenarios

赵阳; 王宪楠; 郭策安; 张健

doi:10.12382/bgxb.2025.0938

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A Multi-Channel Lightweight Small Object Detection Algorithm for Low-Altitude Reconnaissance Scenarios

更新时间：2026-05-07

- A Multi-Channel Lightweight Small Object Detection Algorithm for Low-Altitude Reconnaissance Scenarios
- Acta Armamentarii (2026)
- 作者机构：
  
  沈阳理工大学装备工程学院,辽宁,沈阳,110159
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0938
  CLC： TP391.41
- Received：21 October 2025，
  
  Online First：07 May 2026，
- 稿件说明：
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赵阳，王宪楠，郭策安，等. 面向空基侦察平台的轻量化多通道军事小目标检测方法[J/OL]. 兵工学报, 2026(2026-05-07). https://doi.org/10.12382/bgxb.2025.0938.

ZHAO Y, WANG X N, ZHANG J. A multi-channel lightweight small object detection algorithm for low-altitude reconnaissance scenarios[J/OL]. Acta Armamentarii, 2026(2026-05-07). https://doi.org/10.12382/bgxb.2025.0938. (in Chinese)
赵阳，王宪楠，郭策安，等. 面向空基侦察平台的轻量化多通道军事小目标检测方法[J/OL]. 兵工学报, 2026(2026-05-07). https://doi.org/10.12382/bgxb.2025.0938. DOI：

ZHAO Y, WANG X N, ZHANG J. A multi-channel lightweight small object detection algorithm for low-altitude reconnaissance scenarios[J/OL]. Acta Armamentarii, 2026(2026-05-07). https://doi.org/10.12382/bgxb.2025.0938. (in Chinese) DOI：

摘要

空基侦察平台在复杂战场环境下军事目标通常具有尺寸小、分布密集、背景复杂及尺度变化显著等特征，加之空基与无人机平台受算力与载荷约束，现有检测方法难以同时满足精度与实时性要求。为此，提出一种面向空基与遥感场景的多通道轻量化小目标检测方法。通过改进网络主干结构，引入灰度辅助通道构建彩色-灰度(Red Green Blue Gray

RGB-Gray)四通道输入，以增强对小目标纹理与边缘细节的特征提取能力；在特征融合阶段启用高分辨率的P2检测层，并裁剪低分辨率的P5层，以在保证精度的同时降低模型复杂度；设计基于门控的自适应轻量双分支C3k2模块(Gated-based Adaptive Lightweight Dual-branch Cross Stage Partial Bottleneck with 2 Convolutions

Conditional-C3k2)模块用于替代传统特征提取单元，引入门控机制动态融合主分支与捷径路径，实现特征信息的自适应分配；进一步采用轻量化跨阶段聚合结构以提升多尺度特征整合效率；此外，改进动态检测头，并提出面积加权损失(Varifocal-SIoU-Area-weighted Loss

VSA-Loss)，以优化边界框回归与分类性能。实验结果表明：所提GCSD-YOLOv11n算法在VisDrone2019与军用车辆细粒度检测遥感图像数据集（Military Vehicle Remote Sensing Dataset

MVRSD）上平均精度均值较基准模型分别提升了13.0%与7.4%，其中MVRSD全类精度达到86.4%，同时模型参数量大幅压缩29.4%；该算法在MAR20军事飞机数据集上亦展现出较强的泛化能力，验证了其对复杂军事成像场景下细粒度目标的探测鲁棒性。

Abstract

Military targets in reconnaissance and remote sensing are characterized by small sizes

dense distributions

complex backgrounds

and significant scale variations

while limited computing resources ofunmanned aerial vehicle(UAV)platforms hinder achieving both high accuracy and real-time performance. This paper proposes a multi-channel lightweight detection algorithm for such scenarios. A grayscale auxiliary channel is introduced to construct ared green blue gray(RGB-Gray)four-channel input

enhancing fine-grained texture and edge feature extraction. High-resolution P2 is enabled and low-resolution P5 is pruned to reduce model complexity. Agated-based adaptive lightweight dual-branch cross stage partial bottleneck with 2 convolutions(Conditional-C3k2)module with dynamic gating is designed for adaptive feature fusion

and alightweight cross-stage aggregation structure is adopted to improve multi-scale integration efficiency. Anenhanced dynamic headand avarifocal-SIoU-area-weighted loss(VSA-Loss)function with an area-weighted mechanism optimize performance. GCSD-YOLOv11n improves Mean Average Precisionby 13.0% and 7.4% on VisDrone2019 andmilitary vehicle remote sensing dataset (MVRSD)

respectively

with MVRSD accuracy reaching 86.4% and parameters reducing by 29.4%. Strong generalization on the MAR20 dataset verifies robustness in complex military imaging

providing an effective solution for real-time airborne reconnaissance tasks.

关键词

Keywords

references

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