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基于边缘感知的深度神经网络红外装甲目标检测

盛大俊 张强

盛大俊, 张强. 基于边缘感知的深度神经网络红外装甲目标检测[J]. 红外技术, 2021, 43(8): 784-791.
引用本文: 盛大俊, 张强. 基于边缘感知的深度神经网络红外装甲目标检测[J]. 红外技术, 2021, 43(8): 784-791.
SHENG Dajun, ZHANG Qiang. Infrared Armored Target Detection Based on Edge-perception in Deep Neural Network[J]. Infrared Technology , 2021, 43(8): 784-791.
Citation: SHENG Dajun, ZHANG Qiang. Infrared Armored Target Detection Based on Edge-perception in Deep Neural Network[J]. Infrared Technology , 2021, 43(8): 784-791.

基于边缘感知的深度神经网络红外装甲目标检测

基金项目: 装备预研基金资助课题
详细信息
    作者简介:

    盛大俊(1981-),男,讲师,研究方向:制导测试技术,计算机视觉应用

    通讯作者:

    张强(1975-),男,高工,研究方向:光电系统维修与测试,红外制导技术等,E-mail:x0376y@163.com

  • 中图分类号: TP753

Infrared Armored Target Detection Based on Edge-perception in Deep Neural Network

  • 摘要: 装甲目标自动检测一直是红外制导领域的研究热点与难点。解决该问题的传统方法是提取目标的低层次特征,并对特征分类器进行训练。然而,由于传统的检测算法不能覆盖所有的目标模式,在实际应用中的检测性能受到限制。本文受边缘感知模型的启发,提出了一种基于边缘感知的改进深度网络,该网络是通过边缘感知融合模块提升装甲轮廓精度,利用特征提取模块和上下文聚合模块的优势,能够更好地适应目标的形态变化,具有较高的检测与识别的精度。验证结果表明,本文提出的装甲检测网络模型可以有效地提高红外图像中装甲的检测与定位精度。
  • 图  1  基于边缘感知的目标检测模型框架

    Figure  1.  Model frameworks of object detection based on edge-aware

    图  2  上下文聚合模块

    Figure  2.  Context aggregation module

    图  3  边缘感知融合模块

    Figure  3.  Edge-aware fusion module

    图  4  检测率与FPPI的关系曲线

    Figure  4.  Relationship-curves between detection rate and FPPI

    图  5  不同对比算法的性能对比

    Figure  5.  Performance comparison for different detection algorithms

    图  6  不同对比算法对不同红外图像的装甲检测的结果,其中(a)-(f)分别代表不同图像

    Figure  6.  Results of different comparison algorithms for different images, where (a)-(f) respectively represent the different images

    表  1  装甲检测结果统计

    Table  1.   Statistics of detection results

    Classification Result
    Positive TP(True Positive) FP(False Positive)
    Negative FN(False Negative) TN(True Negative)
    下载: 导出CSV

    表  2  不同测试子集的特点

    Table  2.   Characteristics of different test subsets

    Subset Characteristic
    A The target is fuzzy, the contrast is low, and some turrets are covered.
    B The armored target is small, and there are many bright areas in the background.
    C The armor area is large and full of the whole infrared image.
    D The armor boundary is not obvious and the background is super saturation.
    E The noise is very large and the internal details of the target are uneven.
    F The armored target is fuzzy and partially occlused, and some caterpillar bands are coved by weeds.
    下载: 导出CSV

    表  3  不同测试数据集下检测精度

    Table  3.   Detection Rates under Different Testing Data Sets

    Datasets Models
    SDD DenseNet ResNet ConvNet YOLO-v2 Proposed
    A 73.32% 84.34% 86.61% 85.90% 89.15% 90.06%
    B 62.41% 65.43% 72.15% 77.00% 77.59% 77.14%
    C 69.12% 73.22% 74.35% 72.24% 77.09% 78.07%
    D 78.28% 87.08% 89.40% 86.01% 88.36% 89.44%
    E 51.91% 52.90% 59.75% 59.11% 58.69% 59.58%
    F 48.65% 51.55% 60.11% 52.17% 60.20 60.21%
    下载: 导出CSV
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  • 被引次数: 0
出版历程
  • 收稿日期:  2020-05-10
  • 修回日期:  2020-09-03
  • 刊出日期:  2021-08-20

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