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基于增强型轻量级网络的车载热成像目标检测方法

易诗 周思尧 沈练 朱竞铭

易诗, 周思尧, 沈练, 朱竞铭. 基于增强型轻量级网络的车载热成像目标检测方法[J]. 红外技术, 2021, 43(3): 237-245.
引用本文: 易诗, 周思尧, 沈练, 朱竞铭. 基于增强型轻量级网络的车载热成像目标检测方法[J]. 红外技术, 2021, 43(3): 237-245.
YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. INFRARED TECHNOLOGY, 2021, 43(3): 237-245.
Citation: YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. INFRARED TECHNOLOGY, 2021, 43(3): 237-245.

基于增强型轻量级网络的车载热成像目标检测方法

基金项目: 

国家自然科学基金项目 61771096

国家大学生创新创业项目 S201910616142

详细信息
    作者简介:

    易诗(1983-),男,四川成都人,副教授,高级实验师,主要从事机器视觉研究,深度学习算法研究,信号与信息处理研究。E-mail:549745481@qq.com

  • 中图分类号: TN919.5

Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network

  • 摘要: 车载热成像系统不依赖光源,对天气状况不敏感,探测距离远,对夜间行车有很大辅助作用,热成像自动目标检测对夜间智能驾驶具有重要意义。车载热成像系统所采集的红外图像相比可见光图像具有分辨率低,远距离小目标细节模糊的特点,且热成像目标检测方法需考虑车辆移动速度所要求的算法实时性以及车载嵌入式平台的计算能力。针对以上问题,本文提出了一种针对热成像系统的增强型轻量级红外目标检测网络(Infrared YOLO,I-YOLO),该网络采用(Tiny you only look once,Tiny-YOLO V3)的基础结构,根据红外图像特点,提取浅层卷积层特征,提高红外小目标检测能力,使用单通道卷积核,降低运算量,检测部分使用基于CenterNet结构的检测方式以降低误检测率,提高检测速度。经实际测试,Enhanced Tiny-YOLO目标检测网络在热成像目标检测方面,平均检测率可达91%,检测平均速度达到81Fps,训练模型权重96MB,适宜于车载嵌入式系统上部署。
  • 图  1  Tiny-YOLOV3网络结构

    Figure  1.  Tiny-YOLOV3 network architecture

    图  2  Tiny-YOLOV3检测原理

    Figure  2.  Detection principle of Tiny-YOLOV3

    图  3  I-YOLO网络结构

    Figure  3.  I-YOLO network architecture

    图  4  CenterNet网络结构

    Figure  4.  CenterNet network architecture

    图  5  CenterNet检测原理

    Figure  5.  Detection principle of CenterNet

    图  6  车载红外热成像平台

    Figure  6.  Vehicle infrared thermal imaging platform

    图  7  FLIR红外数据集

    Figure  7.  FLIR Infrared data set

    图  8  模型训练平均损失

    Figure  8.  Average loss of model training

    图  9  实际测试结果

    Figure  9.  Actual test results

    表  1  4类检测目标统计数据分析

    Table  1.   Statistical analysis of four kinds of detection targets

    Detection model Mp(%) Mf(%) Mm(%)
    Person Car Bus Truck Person Car Bus Truck Person Car Bus Truck
    SSD300×300 66 71 73 68 12 13 14 11 21 12 21 20
    RetinaNet-50-500 90 89 88 92 15 17 18 14 6 4 6 14
    Tiny-YOLOV3 65 70 75 69 15 10 15 10 20 15 23 21
    YOLOV3 95 90 90 95 20 18 20 15 5 3 5 15
    I-YOLO 91 88 89 93 3 5 3 5 9 8 6 18
    下载: 导出CSV

    表  2  综合性能测试对比分析

    Table  2.   Comparison and analysis of comprehensive performance tests

    Detection model Mp/% Mf/% Mm/% Mo/FPS Mw/MB
    SSD300×300 67 11 31 13 196
    RetinaNet-50-500 90 15 13 7 246
    Tiny-YOLOV3 66 12 32 62 34
    YOLOV3 95 16 6 21 234
    I-YOLO 91 6 12 81 96
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-09-11
  • 修回日期:  2018-12-21
  • 刊出日期:  2021-04-02

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