Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue

LIU Yuan; ZHAO Jing; JIANG Guoping; XU Fengyu; LU Ningyun

doi:10.11959/j.issn.2096-3750.2024.00390

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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue

Theory and Technology | 更新时间：2025-03-13

- Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue
- Chinese Journal on Internet of Things Vol. 8, Issue 3, Pages: 146-156(2024)
- 作者机构：
  
  1.南京邮电大学自动化学院、人工智能学院，江苏南京 210023
  2.南京航空航天大学航空航天结构力学及控制全国重点实验室，江苏南京 210016
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(51775284);The Open Research Project of the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-05);The Open Research Project of the State Key Laboratory of Aerospace Structural Mechanics and Control, Nanjing University of Aeronautics and Astronautics, China(MCMS-E-0123G04);The Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China(ICT2023B21);The Natural Science Foundation of Nanjing University of Posts and Telecommunications(NY223119)
- DOI：10.11959/j.issn.2096-3750.2024.00390
  CLC： TP391.4
- Received：15 December 2023，
  
  Revised：2024-05-31，
  
  Published：10 September 2024
- 稿件说明：
移动端阅览
刘园,赵静,蒋国平等.融合多尺度和上下文的无人机救援小目标检测算法[J].物联网学报,2024,08(03):146-156.

LIU Yuan,ZHAO Jing,JIANG Guoping,et al.Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue[J].Chinese Journal on Internet of Things,2024,08(03):146-156.
刘园,赵静,蒋国平等.融合多尺度和上下文的无人机救援小目标检测算法[J].物联网学报,2024,08(03):146-156. DOI： 10.11959/j.issn.2096-3750.2024.00390.

LIU Yuan,ZHAO Jing,JIANG Guoping,et al.Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue[J].Chinese Journal on Internet of Things,2024,08(03):146-156. DOI： 10.11959/j.issn.2096-3750.2024.00390.

摘要

针对无人机（UAV

unmanned aerial vehicle）图像中小目标所包含的特征信息少，导致模型检测精度不足的问题，面向无人机海面救援任务提出了一种融合多尺度和上下文信息的图像小目标检测算法。首先，针对小目标特征信息设计上下文增强模块，通过增强特征层的上下文信息，有效地增加了模型对小目标的处理能力。其次，为提高模型的鲁棒性，设计了空间注意力模块加强对重要特征的学习。最后，使用平衡L1损失函数优化基线算法的损失函数，加强了模型检测时的稳定性。基于Tiny-Person数据集，与基准算法进行大量实验对比，所提算法在AP50_tiny上提高了2.06%，一定程度上提高了对海面小目标的检测性能，对救援行动具有积极影响。

Abstract

Aiming at the problem of insufficient feature information contained in small targets under unmanned aerial vehicle (UAV) images that led to insufficient detection accuracy of the model

a small target detection algorithm for UAV sea rescue images that integrated multi-scale and contextual information was proposed. Firstly

context enhancement module was designed for small target feature information

which effectively enhanced the ability of the model to process small targets by enhancing the contextual information of the feature layer. Secondly

to improve the robustness of the model

spatial attention module was designed to enhance the learning of important features. Finally

balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. Based on the Tiny-Person dataset

through extensive experimental comparison with the benchmark algorithm

the proposed algorithm improves the detection performance of small targets on the sea surface by 2.06% on AP50_tiny

which has a positive impact on rescue operations.

关键词

Keywords

references

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