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基于Dense-YOLO网络的井下行人检测模型

张明臻

张明臻. 基于Dense-YOLO网络的井下行人检测模型[J]. 工矿自动化,2022,48(3):86-90.  doi: 10.13272/j.issn.1671-251x.17861
引用本文: 张明臻. 基于Dense-YOLO网络的井下行人检测模型[J]. 工矿自动化,2022,48(3):86-90.  doi: 10.13272/j.issn.1671-251x.17861
ZHANG Mingzhen. Underground pedestrian detection model based on Dense-YOLO network[J]. Journal of Mine Automation,2022,48(3):86-90.  doi: 10.13272/j.issn.1671-251x.17861
Citation: ZHANG Mingzhen. Underground pedestrian detection model based on Dense-YOLO network[J]. Journal of Mine Automation,2022,48(3):86-90.  doi: 10.13272/j.issn.1671-251x.17861

基于Dense-YOLO网络的井下行人检测模型

doi: 10.13272/j.issn.1671-251x.17861
详细信息
    作者简介:

    张明臻(1998-),男,宁夏银川人,硕士研究生,主要研究方向为人工智能与自动化控制,E-mail: zhangmingzhen@outlook.com

  • 中图分类号: TD67

Underground pedestrian detection model based on Dense-YOLO network

  • 摘要: 行人检测是实现矿用车辆无人化的一项关键技术,煤矿井下弱光环境中捕获的图像可见度不佳,极大地影响了行人检测效果。现有行人检测方法忽略了井下弱光环境对目标检测精度的影响,检测效果不理想。针对该问题,提出一种基于Dense-YOLO网络的井下行人检测模型。将弱光图像分解为光照图和反射图,采用Gamma变换、加权对数变换、限制对比度的自适应直方图均衡(CLAHE)对光照图进行增强处理,采用亮度权值和色彩权值对增强后的图像进行加权融合;采用双边滤波算法对反射图进行处理,以增强图像纹理;将增强后的光照图和经过双边滤波处理的反射图逐点相乘,重构出RGB图,并采用ROF去噪模型对融合后的图像进行全局去噪,得到最终的增强图像。将含有残差块的Dense模块添加到YOLOv3中,构建基于Dense-YOLO网络的井下行人检测模型,残差块的加入有利于避免在网络训练过程中出现梯度消失和梯度爆炸等问题。实验结果表明:对弱光图像进行增强处理能够有效提高图像可见度和行人检测效果;Dense-YOLO网络对增强图像的漏检率为4.55%,相较于RetinaNet网络降低了14.91%,基于Dense-YOLO网络的井下行人检测模型有效降低了行人检测漏检率。

     

  • 图  1  弱光图像增强方法

    Figure  1.  Low light image enhancement method

    图  2  弱光图像和增强图像

    Figure  2.  Low light image and enhanced image

    图  3  基于Dense-YOLO网络的井下行人检测模型

    Figure  3.  Underground pedestrian detection model based on Dense-YOLO network

    图  4  弱光环境下行人检测结果对比

    Figure  4.  Comparison of pedestrian detection results in low light environments

    表  1  RetinaNet网络和Dense-YOLO网络检测结果

    Table  1.   Detection results of RetinaNet network and Dense-YOLO network

    图像网络漏检
    率/%
    mAP/%运行
    时间/ms
    弱光
    图像
    RetinaNet96.153.4250.49
    Dense-YOLO60.8131.2451.34
    增强
    图像
    RetinaNet19.4692.6349.55
    Dense-YOLO4.5587.7950.27
    下载: 导出CSV
  • [1] 张小艳,郭海涛. 基于改进混合高斯模型的井下目标检测算法[J]. 工矿自动化,2021,47(4):67-72.

    ZHANG Xiaoyan,GUO Haitao. Underground target detection algorithm based on improved Gaussian mixture model[J]. Industry and Mine Automation,2021,47(4):67-72.
    [2] 董昕宇,师杰,张国英. 基于参数轻量化的井下人体实时检测算法[J]. 工矿自动化,2021,47(6):71-78.

    DONG Xinyu,SHI Jie,ZHANG Guoying. Real-time detection algorithm of underground human body based on lightweight parameters[J]. Industry and Mine Automation,2021,47(6):71-78.
    [3] 谢林江,季桂树,彭清,等. 改进的卷积神经网络在行人检测中的应用[J]. 计算机科学与探索,2018,12(5):708-718. doi: 10.3778/j.issn.1673-9418.1708030

    XIE Linjiang,JI Guishu,PENG Qing,et al. Application of preprocessing convolutional neural network in pedestrian detection[J]. Journal of Frontiers of Computer Science and Technology,2018,12(5):708-718. doi: 10.3778/j.issn.1673-9418.1708030
    [4] 刘备战,赵洪辉,周李兵. 面向无人驾驶的井下行人检测方法[J]. 工矿自动化,2021,47(9):113-117.

    LIU Beizhan,ZHAO Honghui,ZHOU Libing. Unmanned driving-oriented underground mine pedestrian detection method[J]. Industry and Mine Automation,2021,47(9):113-117.
    [5] LIN T Y,P GOYAL P,GIRSHICK R,et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(2):318-327. doi: 10.1109/TPAMI.2018.2858826
    [6] HE Kaiming,SUN Jian,TANG Xiaoou,et al. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353. doi: 10.1109/TPAMI.2010.168
    [7] FU Xueyang,ZENG Delu,HUANG Yue,et al. A fusion-based enhancing method for weakly illuminated images[J]. Signal Processing,2016,129:82-96. doi: 10.1016/j.sigpro.2016.05.031
    [8] WANG Shuhang,ZHENG Jin,HU Haimiao,et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing,2013,22(9):3538-3548. doi: 10.1109/TIP.2013.2261309
    [9] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[EB/OL]. (2016-05-09) [2021-10-10]. https://arxiv.org/pdf/1506.02640.pdf.
    [10] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. ( 2018-04-08) [2021-10-10]. https://arxiv.org/pdf/1804.02767.pdf.
    [11] HUANG Gao, LIU Zhuang, MAATEN L V D, et al. Densely connected convolutional networks[EB/OL]. ( 2018-01-28)[2021-10-10]. https://arxiv.org/pdf/1608.06993.pdf.
    [12] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[EB/OL]. (2015-02-21)[2021-10-10]. https://arxiv.org/pdf/1405.0312.pdf.
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出版历程
  • 收稿日期:  2021-11-05
  • 修回日期:  2022-03-09
  • 网络出版日期:  2022-03-18

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