HAN Zhongli. Mining safety helmet wearing detection based on convolutional neural network[J]. Journal of Mine Automation, 2024, 50(S1): 82-87.
Citation: HAN Zhongli. Mining safety helmet wearing detection based on convolutional neural network[J]. Journal of Mine Automation, 2024, 50(S1): 82-87.

Mining safety helmet wearing detection based on convolutional neural network

More Information
  • Received Date: January 24, 2024
  • [1]
    杜青, 杨仕教, 郭钦鹏, 等.地下矿山作业人员佩戴安全帽智能检测方法[J].工矿自动化, 2023, 49(7):134-140.
    [2]
    王国法, 杜毅博, 任怀伟, 等.智能化煤矿顶层设计研究与实践[J].煤炭学报, 2020, 45(6):1909-1924.
    [3]
    王国法.煤矿智能化最新技术进展与问题探讨[J].煤炭科学技术, 2022, 50(1):1-27.
    [4]
    程德强, 寇旗旗, 江鹤, 等.全矿井智能视频分析关键技术综述[J].工矿自动化, 2023, 49(11):1-21.
    [5]
    贾峻苏, 鲍庆洁, 唐慧明.基于可变形部件模型的安全头盔佩戴检测[J].计算机应用研究, 2016, 33(3):953-956.
    [6]
    PARK M W, ELSAFTY N, ZHU Zhenhua.Hardhat-wearing detection for enhancing on-site safety of construction workers[J].Journal of Construction Engineering and Management, 2015, 141(9).DOI: 10.1061/(ASCE)CO.1943-7862.0000974.
    [7]
    GU Yuwan, WANG Yusheng, SHI Lin, et al.Automatic detection of safety helmet wearing based on head region location[J].IET Image Processing, 2021, 15(11):2441-2453.
    [8]
    GIRSHICK R B, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[J].CoRR, 2013, abs/1311.2524.
    [9]
    GIRSHICK R.Fast R-CNN[J].Computer Science, 2015:1440-1448.
    [10]
    REN Shaoqing, HE Kaiming, GIRSHICK R, et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
    [11]
    DAI Jifeng, LI Yi, HE Kaiming, et al.R-FCN:object detection via region-based fully convolutional networks[J].CoRR, 2016, abs/1605.06409.
    [12]
    REDMON J, DIVVALA S, GIRSHICK R, et al.You Only Look Once:unified, real-time object detection[J].CoRR, 2015, abs/1506.02640.
    [13]
    唐聪, 凌永顺, 郑科栋, 等.基于深度学习的多视窗SSD目标检测方法[J].红外与激光工程, 2018, 47(1):302-310.
    [14]
    徐守坤, 王雅如, 顾玉宛.基于改进区域卷积神经网络的安全帽佩戴检测[J].计算机工程与设计, 2020, 41(5):1385-1389.
    [15]
    李天宇, 李栋, 陈明举, 等.一种高精度的卷积神经网络安全帽检测方法[J].液晶与显示, 2021, 36(7):1018-1026.
    [16]
    张春堂, 管利聪.基于SSD-MobileNet的矿工安保穿戴设备检测系统[J].工矿自动化, 2019, 45(6):96-100.
    [17]
    李忠飞, 冯仕咏, 郭骏, 等.融合坐标注意力与多尺度特征的轻量级安全帽佩戴检测[J].工矿自动化, 2023, 49(11):151-159.
    [18]
    刘赟.ReLU激活函数下卷积神经网络的不同类型噪声增益研究[D].南京:南京邮电大学, 2023.
    [19]
    KONIG D, ADAM M, JARVERS C, et al.Fully convolutional region proposal networks for multispectral person detection[J].IEEE, 2017.DOI: 10.1109/CVPRW.2017.36.
    [20]
    ROSARIO B L, WEISSFELD L A, LAYMON C M, et al.Inter-rater reliability of manual and automated region-of-interest delineation for PiB PET[J].Neuroimage, 2011, 55(3):933-941.
    [21]
    赵云龙, 田生祥, 李岩, 等.基于注意力模型和Soft-NMS的输电线路小目标检测方法[J].电子科技大学学报, 2023, 52(6):906-914.
    [22]
    REZATOFIGHI H, TSOI N, GWAK J Y, et al.Generalized intersection over union:a metric and a loss for bounding box regression[C].2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Los Angeles, 2019.
  • Cited by

    Periodical cited type(3)

    1. 李泉新,程卓尔,方俊,牟全斌,刘飞,丛琳. 定向长钻孔瓦斯抽采负压变化规律及监测控制技术研究进展. 煤田地质与勘探. 2024(11): 171-182 .
    2. 陈鹏,彭是阳,陈西华,高圆圆. 基于负压测试的新田煤矿瓦斯抽采技术优化研究. 华北科技学院学报. 2023(05): 15-23+35 .
    3. 鲁义,谷旺鑫,丁仰卫,李修磊,李亮. 固结软煤层瓦斯抽采钻孔周围裂隙的弹性胶结材料研制. 煤炭科学技术. 2022(02): 129-136 .

    Other cited types(3)

Catalog

    Article Metrics

    Article views (3) PDF downloads (0) Cited by(6)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return