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基于VSA-YOLO v11n的无人机红外小目标检测

MA Tao XIONG Yingzhuo PAN Qingna YANG Haitao WANG Huapeng YAN Xiai CHEN Rui

红外技术2025,Vol.47Issue(12):1510-1517,8.
红外技术2025,Vol.47Issue(12):1510-1517,8.

基于VSA-YOLO v11n的无人机红外小目标检测

Infrared Small Object Detection for UAV Imagery Based on VSA-YOLOv11n

MA Tao 1XIONG Yingzhuo 1PAN Qingna 1YANG Haitao 2WANG Huapeng 3YAN Xiai 1CHEN Rui1

作者信息

  • 1. Department of Investigation,Hunan Police Academy,Changsha 410138,China
  • 2. Department of Investigation,Hunan Police Academy,Changsha 410138,China||Video and Audio Material Examination Department,Criminal Investigation Police University of China,Shenyang 110854,China
  • 3. Video and Audio Material Examination Department,Criminal Investigation Police University of China,Shenyang 110854,China
  • 折叠

摘要

Abstract

In this study,we propose an enhanced lightweight detection model named VSA-YOLOv11n to address the challenges of detecting small objects in infrared imagery collected by uncrewed aerial vehicles(UAVs)such as weak thermal signatures,complex background interference,and significant scale variation.The proposed model is based on the YOLOv11n architecture and integrates a streamlined and efficient backbone network called VanillaNet to improve feature extraction capability while significantly reducing inference latency.A structured multi-scale convolutional module is introduced in the feature fusion stage to enhance the model's sensitivity to targets of varying sizes in cluttered backgrounds.Furthermore,an adaptive spatial feature fusion(ASFF)head is incorporated to enable fine-grained semantic aggregation and selective enhancement across scales to improve detection accuracy and robustness for small infrared targets.The results of extensive experiments conducted on the HIT-UAV infrared small object dataset demonstrate that the proposed model achieved comprehensive improvements in terms of accuracy,inference speed,and parameter efficiency.Specifically,the model attained an mAP50 of 81.3%with an inference latency of only 1.79 ms,which thus outperformed existing mainstream lightweight detectors.These results highlight the model's strong practical applicability and deployment potential,particularly in low-altitude infrared scenarios with high real-time requirements.

关键词

红外图像/无人机/小目标检测/YOLOv11/特征融合

Key words

infrared imagery/UAV/small object detection/YOLOv11/feature fusion

分类

信息技术与安全科学

引用本文复制引用

MA Tao,XIONG Yingzhuo,PAN Qingna,YANG Haitao,WANG Huapeng,YAN Xiai,CHEN Rui..基于VSA-YOLO v11n的无人机红外小目标检测[J].红外技术,2025,47(12):1510-1517,8.

基金项目

公安部科技计划项目(2023YY21) (2023YY21)

湖南省社会科学成果评审委员会课题(XSP2025WTY003) (XSP2025WTY003)

湖南省重点研发计划(2024AQ2023,2024AQ2024) (2024AQ2023,2024AQ2024)

湖南省教育厅重点项目(23A0705,22A0687) (23A0705,22A0687)

湖南省普通高等学校教学改革研究项目(HNJG-2021-1151) (HNJG-2021-1151)

湖南省哲学社会科学基金(22JD077,19YBA142). (22JD077,19YBA142)

红外技术

OA北大核心

1001-8891

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