PCA-YOLO11:Lightweight Object Detection in Multi-altitude Complex Environments

Xiangyang SU; Yang WANG; Senqi TAN; Huazhen CHU; Bo PAN

doi:10.12382/bgxb.2025.0584

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PCA-YOLO11:Lightweight Object Detection in Multi-altitude Complex Environments

更新时间：2026-02-10

- PCA-YOLO11:Lightweight Object Detection in Multi-altitude Complex Environments
- Acta Armamentarii Vol. 46, Issue S2, Pages: 250584(2025)
- 作者机构：
  
  1. 中兵智能创新研究院有限公司, 北京 100072
  2. 群体协同与自主实验室, 北京 100072
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0584
  CLC：
- Received：01 July 2025，
  
  Online First：03 February 2026，
  
  Published：2025
- 稿件说明：
移动端阅览
Xiangyang SU, Yang WANG, Senqi TAN, et al. PCA-YOLO11:Lightweight Object Detection in Multi-altitude Complex Environments[J]. Acta Armamentarii, 2025, 46(S2): 250584.
DOI：

Xiangyang SU, Yang WANG, Senqi TAN, et al. PCA-YOLO11:Lightweight Object Detection in Multi-altitude Complex Environments[J]. Acta Armamentarii, 2025, 46(S2): 250584. DOI： 10.12382/bgxb.2025.0584.

摘要

面向复杂环境中无人机对目标的实时精准检测需求

提出一种全域复杂环境的轻量化检测模型PCA-YOLO11(Pinwheel-shaped Context Aggregation YOLO11)。构建涵盖多高度、多视角及复杂背景干扰的全域视角特种车辆数据集

包含1311张图像与2729个特种车辆目标;设计融合风车形卷积与全局上下文聚合的特征增强模块

通过局部细节特征感知强化小目标特征提取与抗遮挡能力

并基于长程依赖建模同步增强抗背景干扰鲁棒性;引入尺度感知的动态损失函数

优化多尺度目标的定位精度;采用约束训练导向的层级通道剪枝策略

实现精度可控的轻量化。实验结果表明:PCA-YOLO11在自制数据集上的mAP@0.5-0.95达79.17%

较基础模型提高3.01%;轻量化版本参数量降低14.4%

精度损失仅0.45%

推理速度达128.8FPS

满足实时精准检测需求。

Abstract

To address the requirements for the real-time accurate detection of targets by UAVs

this paper proposes pinwheel-shaped context aggregation (PCA)-YOLO11 (PCA-YOLO11) model

a lightweight target detection model in multi-altitude complex environments.A comprehensive special vehicle dataset spanning the multi-altitude views with complex background interference is constructed

comprising 1 311 images and 2 729 targets.A feature enhancement module pinwheel-shaped context aggregation convolution integrating pinwheel-shaped convolution and global context aggregation is designed

which enhances small-target feature extraction and occlusion resistance through local detail perception while enhancing anti-background-interference robustness via long-range dependency modeling.Subsequently

a scale-based dynamic loss function is introduced to optimize the multi-scale target localization accuracy.Finally

a constrained training-guided hierarchical channel pruning strategy is used to achieve the compression of accuracy-preserving model.Experimental results demonstrate that PCA-YOLO11 achieves 79.17% mAP@0.5-0.95 on the custom dataset

outperforming the baseline model by 3.01%.The lightweight version reduces parameters by 14.4% with accuracy degradation of merely 0.45% and reaches 128.8 FPS inference speed

meeting the real-time accurate detection requirements.

关键词

Keywords

references

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