面向低空场景下的鸟类小目标精准检测方法

张波; 陈明理; 周小波; 程培昊; 夏琳琳

doi:10.12382/bgxb.2025.1080

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面向低空场景下的鸟类小目标精准检测方法

更新时间：2026-04-21

- 面向低空场景下的鸟类小目标精准检测方法
- Accurate Detection Method for Small Bird Targets in Low-altitude Scenarios
- 兵工学报 2026年
- 作者机构：
  
  东北电力大学自动化工程学院,吉林,吉林,132012
- 作者简介：
- 基金信息：
  
  吉林省发改委产业技术研究与开发项目(2024C007-3)
- DOI：10.12382/bgxb.2025.1080
  中图分类号： V351.3
- 收稿：2025-12-08，
  
  网络首发：2026-04-21，
- 稿件说明：
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张波，陈明理，周小波，等. 面向低空场景下的鸟类小目标精准检测方法[J/OL]. 兵工学报, 2026(2026-04-21). https://doi.org/10.12382/bgxb.2025.1080.

ZHANG B, CHEN M L, ZHOU X B, et al. Accurate detection method for small bird targets in low-altitude scenarios[J/OL]. Acta Armamentarii, 2026(2026-04-21). https://doi.org/10.12382/bgxb.2025.1080. (in Chinese)
张波，陈明理，周小波，等. 面向低空场景下的鸟类小目标精准检测方法[J/OL]. 兵工学报, 2026(2026-04-21). https://doi.org/10.12382/bgxb.2025.1080. DOI：

ZHANG B, CHEN M L, ZHOU X B, et al. Accurate detection method for small bird targets in low-altitude scenarios[J/OL]. Acta Armamentarii, 2026(2026-04-21). https://doi.org/10.12382/bgxb.2025.1080. (in Chinese) DOI：

摘要

针对航天发射场及机场共有的低空复杂背景下鸟类小目标识别难、易发生鸟击事件、威胁航空安全与人员生命等问题，提出一种基于YOLOv8n的深度增强尺度感知网络DESN-YOLOv8n(Deeply Enhanced Scale-aware Networkbasedon YOLOv8n)，专注于鸟类小目标检测。该模型通过构建主干网络特征融合模块C2F_DCA，利用深度可分离卷积与高效通道注意力机制增强空间特征提取能力，放大超小目标微弱特征。同时考虑颈部网络Concat模块直接拼接浅、深层特征，关键特征易随上下采样丢失问题，引入自适应空间特征融合模块，以聚焦小目标有效区域。在损失计算方面，使用归一化Wasserstein距离损失函数，将边界框建模为2D高斯分布，有效防止漏检和定位偏移。公共数据集AirBirds上的实验结果表明，新模型在小目标检测精度、特征增强能力和回归优化效果方面均取得显著提升。

Abstract

To address the challenge of detecting small bird targets in low-altitude complex backgrounds—common in aerospace launch sites and airports-where such targets are difficult to identify and bird strike incidents pose significant threats to aviation safety and human lives

this paper proposesa deeply enhanced scale-aware network based on YOLOv8n(DESN-YOLOv8n)

specifically designedfor small bird target detection.Themodelconstructs a backbone feature fusion moduletermedC2F_DCA

which integrates depthwise separable convolutionsand an efficient channel attentionmechanism to enhance spatial feature extraction and amplify subtle characteristics of ultra-small targets. Additionally

to address the issuethatdirect concatenation of shallow and deep features in the neck’s Concat module may lead to the loss of critical information during upsampling and downsampling

an adaptive spatial feature fusionmodule is introduced to focus on salient regions of small targets.In terms of loss computation

a normalized Wasserstein distanceloss function is employed

which models bounding boxes as 2D Gaussian distributions to effectively mitigatemissed detectionsandlocalization deviation.Experimental results on the public dataset AirBirds demonstrate that the proposed model achieves significant improvements in small target detection accuracy

feature enhancement capability

and regression optimization performance.

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