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红外热成像中低分辨率行人小目标检测方法

胡焱 胡皓冰 赵宇航 原子昊 司成可

胡焱, 胡皓冰, 赵宇航, 原子昊, 司成可. 红外热成像中低分辨率行人小目标检测方法[J]. 红外技术, 2022, 44(11): 1146-1153.
引用本文: 胡焱, 胡皓冰, 赵宇航, 原子昊, 司成可. 红外热成像中低分辨率行人小目标检测方法[J]. 红外技术, 2022, 44(11): 1146-1153.
HU Yan, HU Haobing, ZHAO Yuhang, YUAN Zihao, SI Chengke. Infrared Thermal Imaging Low-Resolution and Small Pedestrian Target Detection Method[J]. Infrared Technology , 2022, 44(11): 1146-1153.
Citation: HU Yan, HU Haobing, ZHAO Yuhang, YUAN Zihao, SI Chengke. Infrared Thermal Imaging Low-Resolution and Small Pedestrian Target Detection Method[J]. Infrared Technology , 2022, 44(11): 1146-1153.

红外热成像中低分辨率行人小目标检测方法

基金项目: 

国家自然科学基金项目 62061003

四川省科技计划重点研发项目 2021YFG0192

省级大学生创新创业项目 S202110624176

详细信息
    作者简介:

    胡焱(1973-),男,四川大英人,教授,研究生导师,研究方向:航空电子设备维修、测控。Email:huyan@cafuc.edu.cn

    通讯作者:

    胡皓冰(1997-),男,硕士研究生,主要从事深度学习目标检测的研究。Email:191650964@qq.com

  • 中图分类号: TP391.4

Infrared Thermal Imaging Low-Resolution and Small Pedestrian Target Detection Method

  • 摘要: 红外热成像图像的目标检测中,针对低分辨率小目标检测效果差、复杂尺度目标检测率低等问题,提出一种基于改进YOLOv5的红外低分辨率目标检测算法。选用LLVIP红外数据集,通过引入不同注意力机制来对比检测效果。选用效果最佳的注意力机制,改进目标检测网络的损失函数提高对小目标的检测率。利用TiX650热成像仪采集小目标图像样本对原数据集进行优化采样和增广,分别使用改进前后的YOLOv5网络进行训练。从模型训练结果和目标检测结果评估模型的性能提升,实验结果表明:相较于原始训练模型,改进后YOLOv5的训练模型,在红外成像的同一场景中对低分辨率小目标的检测精度上有明显提升,且漏检率低。
  • 图  1  实验方案设计流程图

    Figure  1.  Flow chart of experimental scheme design

    图  2  YOLOv5s 6.0结构

    Figure  2.  YOLOv5s 6.0 structure

    图  3  CIoULoss损失函数示意图

    Figure  3.  CIoULoss function diagram

    图  4  主干网络修改示意图

    Figure  4.  Schematic diagram of backbone network modification

    图  5  三种注意力机制的示意图

    Figure  5.  Schematic diagram of the three attention mechanisms

    图  6  两种场景下1组与8组模型的目标检测结果对比

    Figure  6.  Detection results of Group1 and 8 models in two scenarios

    图  7  检测数随帧数变化对比

    Figure  7.  Comparison of recognition number with frame rate

    表  1  红外数据集对比[9]

    Table  1.   Comparison of infrared datasets

    Number of image pairs
    (1 frame selected per second)
    Resolution Aligned Camera angle Low-light Pedestrian
    TNO 261 768×576 shot on the ground few few
    INO 2100 328×254 surveillance few
    OSU 285 320×240 surveillance ×
    CVC-14 849 640×512 × driving
    KAIST 4750 640×480 driving
    FILR 5258 640×512 × driving
    LLVIP 15488 1080×720 surveillance
    下载: 导出CSV

    表  2  LLVIP对比优化数据集

    Table  2.   Comparison LLVIP with optimized dataset

    Number of images Resolution Aligned Camera angle Low-light Pedestrian
    Original 15488 1080×720 surveillance
    Sampling 3900 1080×720 surveillance
    Addition 356 640×480 shot on the ground
    下载: 导出CSV

    表  3  几种目标检测算法的性能对比

    Table  3.   Performance comparison of several target detection algorithms

    Algorithm Infrastructure Image Size mAP50(VOC07+12) mAP50(COCO) FPS(Titan X)
    Faster R-CNN VGG-16 300×300 73.2 42.7 7
    SSD300 VGG-16 300×300 74.3 41.2 46
    YOLOv3 DarkNet-53 416×416 78.3 55.3 34
    YOLOv5l CSPDarknet-53 640×640 68.5 50.4 97
    下载: 导出CSV

    表  4  YOLOv5 6.0对比YOLOv5 5.0性能对比

    Table  4.   YOLOv5 6.0 vs YOLOv5 5.0 performance improvement

    YOLOv5l
    (Large)
    Size/pixels mAPval
    0.5:0.95
    mAPval
    0.5
    Speed
    CPU b1/ms
    Speed
    V100 b1/ms
    Speed
    V00 b32/ms
    Params
    (M)
    FLOPs
    [@640]
    (B)
    v5.0(previous) 640 48.2 66.9 457.9 11.6 2.8 47 115.4
    v6.0(this release) 640 48.8 67.2 424.5 10.9 2.7 46.5 109.1
    下载: 导出CSV

    表  5  红外热成像仪主要参数

    Table  5.   Fluke TiX650 main parameters table

    Main parameters TiX650
    Infrared resolution 640×480(307, 200 pixels)
    IFOV/mrad 0.87
    Field angle/° 32×24
    Infrared spectral/μm 8~14
    Temperature measurement range -40℃~2000℃(-40℉~3632℉)
    Accuracy ±1℃ or 1% at 25℃ ambient temperature
    下载: 导出CSV

    表  6  不同改进方法对平均识别精度的影响

    Table  6.   Effect of different improvement methods on the mAP

    No. Dataset Replace C3 Before SPPF by Replace non max suppression by mAP0.5
    CBAM SE Coordinate
    Attention
    CIoU_nms
    1 Original 98.2%
    2 98.2%
    3 98.4%
    4 98.4%
    5 98.5%
    6 97.2%
    7 Improved 97.4%
    8 97.6%
    下载: 导出CSV

    表  7  测试结果的性能对比

    Table  7.   Comparison of test results performance

    Numbers in scenario (a) Numbers in scenario (b) Average numbers per frame Average detection rate(ms/fps)
    Result 1 3 3 2.53 6
    Result 2 9 11 6.08 6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-24
  • 修回日期:  2022-06-23
  • 刊出日期:  2022-11-20

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