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基于改进YOLOv5的复杂背景红外弱小目标检测算法

代牮 赵旭 李连鹏 刘文 褚昕悦

代牮, 赵旭, 李连鹏, 刘文, 褚昕悦. 基于改进YOLOv5的复杂背景红外弱小目标检测算法[J]. 红外技术, 2022, 44(5): 504-512.
引用本文: 代牮, 赵旭, 李连鹏, 刘文, 褚昕悦. 基于改进YOLOv5的复杂背景红外弱小目标检测算法[J]. 红外技术, 2022, 44(5): 504-512.
DAI Jian, ZHAO Xu, LI Lianpeng, LIU Wen, CHU Xinyue. Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background[J]. Infrared Technology , 2022, 44(5): 504-512.
Citation: DAI Jian, ZHAO Xu, LI Lianpeng, LIU Wen, CHU Xinyue. Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background[J]. Infrared Technology , 2022, 44(5): 504-512.

基于改进YOLOv5的复杂背景红外弱小目标检测算法

基金项目: 

国家重点研发计划课题 2020YFC1511702

国家自然科学基金 61771059

详细信息
    作者简介:

    代牮(1997-),男,硕士研究生,主要从事红外与激光双模复合探测技术的研究。E-mail:hbjsdj970321@163.com

    通讯作者:

    赵旭(1988-),男,博士,硕士生导师,清华大学访问学者,主要从事激光红外复合近场探测,导航制导与控制方面的研究。E-mail:zhaoxu@bistu.edu.cn

  • 中图分类号: TP391

Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background

  • 摘要: 针对传统算法依赖于对红外目标与环境背景的精确分离和信息提取,难以满足复杂背景和噪声等干扰因素下的检测需求。论文提出一种基于改进YOLOv5(You Only Look Once)的复杂背景红外弱小目标检测算法。该算法在YOLOv5基础上,添加注意力机制提高算法的特征提取能力和检测效率,同时改进原YOLOv5目标检测网络的损失函数和预测框的筛选方式提高算法对红外弱小目标检测的准确率。实验选取了来自不同复杂背景的7组红外弱小目标数据集,将这些图像数据集进行标注并训练,得到红外弱小目标检测模型,然后从模型训练结果和目标检测结果的角度评估算法和模型的正确性。实验结果表明:改进的YOLOv5算法训练出来的模型,检测准确性和检测速度对比实验列出的几种目标检测算法均有明显的提升,平均精度均值(mean Average Precision,mAP)可达99.6%以上,在不同复杂背景下均可有效检测出红外弱小目标,且漏警率、虚警率低。
  • 图  1  YOLOv5网络模型

    Figure  1.  YOLOv5 network model

    图  2  Neck网络结构

    Figure  2.  Neck network structure

    图  3  IOU示意图

    Figure  3.  IOU schematic diagram

    图  4  GIOU示意图

    Figure  4.  GIOU schematic diagram

    图  5  改进YOLOv5目标检测算法

    Figure  5.  Improved YOLOv5 target detection algorithm

    图  6  注意力机制SE模块

    Figure  6.  Attention mechanism SE module

    图  7  GIOU无法识别预测框位置图

    Figure  7.  GIOU cannot identify the prediction box location map

    图  8  CIOU示意图

    Figure  8.  CIOU schematic diagram

    图  9  训练数据集标注

    Figure  9.  Training data set annotation

    图  10  目标检测准确率

    Figure  10.  Target detection accuracy

    图  11  目标检测召回率

    Figure  11.  Target detection recall

    图  12  平均精度均值(mAP@0.5)

    Figure  12.  Mean average precision (mAP@0.5)

    图  13  损失函数CIOU_Loss

    Figure  13.  The loss function CIOU_Loss

    图  14  测试集检测结果

    Figure  14.  Test set detection results

    表  1  红外图像数据划分

    Table  1.   Infrared image data segmentation

    Dataset Total Training set Validation set Test set
    data1 400 280 80 40
    data2 400 280 80 40
    data3 500 350 100 50
    data4 500 350 100 50
    data5 600 420 120 60
    data6 600 420 120 60
    data7 600 420 120 60
    下载: 导出CSV

    表  2  训练环境配置

    Table  2.   Training environment configuration

    Parameters Configuration
    Operating system ubuntu16.04
    Video memory 8G
    RAM 8G
    GPU NVIDIA GeForce GTX 1050 Ti
    GPU acceleration environment Training framework CUDA10.1
    Pytorch
    下载: 导出CSV

    表  3  训练参数配置

    Table  3.   Training parameters configuration

    Parameters Configuration
    Model YOLOv5s
    Training rounds 50
    Batch size 8
    Weights Yolov5s.pt
    下载: 导出CSV

    表  4  算法模型结果对比

    Table  4.   Comparison of 5 model results

    Parameters mAP Time/s
    SSD 78.88% 5.80
    Faster R-CNN 90.33% 4.89
    YOLOv3 97.55% 2.47
    YOLOv5
    Improved YOLOv5
    98.76%
    99.65%
    1.25
    0.82
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-18
  • 修回日期:  2021-10-21
  • 刊出日期:  2022-05-20

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