Citation: | CHENG Deqiang, KOU Qiqi, JIANG He, et al. Overview of key technologies for mine-wide intelligent video analysis[J]. Journal of Mine Automation,2023,49(11):1-21. doi: 10.13272/j.issn.1671-251x.18165 |
[1] |
王国法,杜毅博. 智慧煤矿与智能化开采技术的发展方向[J]. 煤炭科学技术,2019,47(1):1-10.
WANG Guofa,DU Yibo. Development direction of intelligent coal mine and intelligent mining technology[J]. Coal Science and Technology,2019,47(1):1-10.
|
[2] |
王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采关键核心技术分析[J]. 煤炭学报,2019,44(1):34-41.
WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41.
|
[3] |
刘峰,曹文君,张建明,等. 我国煤炭工业科技创新进展及“十四五”发展方向[J]. 煤炭学报,2021,46(1):1-15.
LIU Feng,CAO Wenjun,ZHANG Jianming,et al. Current technological innovation and development direction of the 14(th) Five-Year Plan period in China coal industry[J]. Journal of China Coal Society,2021,46(1):1-15.
|
[4] |
LALATENDU M,DEVI P M,PRASANTA K J. Application of wireless sensor network for environmental monitoring in underground coal mines:A systematic review[J]. Journal of Network and Computer Applications,2018,106:48-67. doi: 10.1016/j.jnca.2017.12.022
|
[5] |
刘峰,曹文君,张建明. 持续创新70年硕果丰盈——煤炭工业70年科技创新综述[J]. 中国煤炭,2019,45(9):5-12.
LIU Feng,CAO Wenjun,ZHANG Jianming. 70 years of continuous innovation and fruitfulness-an overview of 70 years of scientific and technological innovation in the coal industry[J]. Journal of China Coal,2019,45(9):5-12.
|
[6] |
王国法,刘峰,庞义辉,等. 煤矿智能化——煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357.
WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357.
|
[7] |
姜德义,魏立科,王翀,等. 智慧矿山边缘云协同计算技术架构与基础保障关键技术探讨[J]. 煤炭学报,2020,45(1):484-492.
JIANG Deyi,WEI Like,WANG Chong,et al. Discussion on the technology architecture and key basic support technology for intelligent mine edge-cloud collaborative computing[J]. Journal of China Coal Society,2020,45(1):484-492.
|
[8] |
YAO Haifei,WANG Haiyan,LI Yanchuan,et al. Three-dimensional spatial and temporal distributions of dust in roadway tunneling[J]. International Journal of Mining Science and Technology,2020,7(1):88-96.
|
[9] |
LECCA M. STAR:a segmentation-based approximation of point-based sampling milano retinex for color image enhancement[J]. IEEE Transactions on Image Processing,2018,27(12):5802-5812. doi: 10.1109/TIP.2018.2858541
|
[10] |
蔡文郁,张美燕,吴岩,等. 基于循环生成对抗网络的超分辨率重建算法研究[J]. 电子与信息学报,2022,44(1):178-186.
CAI Wenyu,ZHANG Meiyan,WU Yan,et al. Research on cyclic generation countermeasure network based super-resolution image reconstruction algorithm[J]. Journal of Electronics & Information Technology,2022,44(1):178-186.
|
[11] |
WANG Jinbao,LU Ke,XUE Jian,et al. Single image dehazing based on the physical model and MSRCR algorithm[J]. IEEE Transactions on Circuits and Systems for Video Technology,2018,28(9):2190-2199. doi: 10.1109/TCSVT.2017.2728822
|
[12] |
张立亚,郝博南,孟庆勇,等. 基于HSV空间改进融合Retinex算法的井下图像增强方法[J]. 煤炭学报,2020,45(增刊1):532-540.
ZHANG Liya,HAO Bonan,MENG Qingyong,et al. Method of image enhancement in coal mine based on improved Retinex fusion algorithm in HSV space[J]. Journal of China Coal Society,2020,45(S1):532-540.
|
[13] |
JIANG He,YANG Jie. In-place similarity and its applications in image and video detail enhancement[J]. Electronics Letters,2016,52(12):1022-1024. doi: 10.1049/el.2015.3876
|
[14] |
张谢华,张申,方帅,等. 煤矿智能视频监控中雾尘图像的清晰化研究[J]. 煤炭学报,2014,39(1):198-204.
ZHANG Xiehua,ZHANG Shen,FANG Shuai,et al. Clearing research on fog and dust images in coalmine intelligent video surveillance[J]. Journal of China Coal Society,2014,39(1):198-204.
|
[15] |
应东杰,李文节. 煤矿监控图像增强算法的分析与实现[J]. 工矿自动化,2012,38(8):55-58.
YING Dongjie,LI Wenjie. Analysis of enhancement algorithms of coal mine monitoring image and its realization[J]. Industry and Mine Automation,2012,38(8):55-58.
|
[16] |
程德强,郑珍,姜海龙. 一种煤矿井下图像增强算法[J]. 工矿自动化,2015,41(12):31-34.
CHENG Deqiang,ZHENG Zhen,JIANG Hailong. An image enhancement algorithm for coal mine underground[J]. Industry and Mine Automation,2015,41(12):31-34.
|
[17] |
范伟强,刘毅. 基于自适应小波变换的煤矿降质图像模糊增强算法[J]. 煤炭学报,2020,45(12):4248-4260.
FAN Weiqiang,LIU Yi. Fuzzy enhancement algorithm of coal mine degradation image based on adaptive wavelet transform[J]. Journal of China Coal Society,2020,45(12):4248-4260.
|
[18] |
唐守锋,史可,仝光明,等. 一种矿井低照度图像增强算法[J]. 工矿自动化,2021,47(10):32-36.
TANG Shoufeng,SHI Ke,TONG Guangming,et al. A mine low illumination image enhancement algorithm[J]. Industry and Mine Automation,2021,47(10):32-36.
|
[19] |
JIANG He,ASAD M,HUANG Xiaolin,et al. Learning in-place residual homogeneity for single image detail enhancement[J]. Journal of Electronic Imaging,2020,29:16-41.
|
[20] |
DU Yuxin,TONG Minming,ZHOU Lingling,et al. Edge detection based on Retinex theory and wavelet multiscale product for mine images[J]. Applied Optics,2016,55:9625-9637. doi: 10.1364/AO.55.009625
|
[21] |
WANG Lingfeng,XIANG Shiming,MENG Gaofeng,et al. Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation[J]. IEEE Transactions on Circuits and Systems for Video Technology,2013,23(8):1289-1299. doi: 10.1109/TCSVT.2013.2240915
|
[22] |
汪海涛,于文洁,张光磊. 基于在线多字典学习的矿井图像超分辨率重建方法[J]. 工矿自动化,2020,46(9):74-78.
WANG Haitao,YU Wenjie,ZHANG Guanglei. Super-resolution reconstruction method of mine image based on online multi-dictionary learning[J]. Industry and Mine Automation,2020,46(9):74-78.
|
[23] |
程德强,于文洁,郭昕,等. 自适应的图像在线字典学习超分辨率重建算法[J]. 激光与光电子学进展,2020,57(6):302-312.
CHENG Deqiang,YU Wenjie,GUO Xin,et al. Super-resolution reconstruction algorithm based on adaptive image online dictionary learning[J]. Laser & Optoelectronics Progress,2020,57(6):302-312.
|
[24] |
CAI Huangkai,JIANG He,HUANG Xiaolin,et al. Violence detection based on spatio-temporal feature and fisher vector[C]. Chinese Conference on Pattern Recognition and Computer Vision,Guangzhou,2018:180-190.
|
[25] |
REN Chao,HE Xiaohai,TENG Qizhi,et al. Single image super-resolution using local geometric duality and non-local similarity[J]. IEEE Transactions on Image Processing,2016,25(5):2168-2183. doi: 10.1109/TIP.2016.2542442
|
[26] |
JIANG He,ZHAI Guangtao,CAI Huangkai,et al. Scalable motion analysis based surveillance video de-noising[C]. IEEE International Conference on Multimedia & Expo Workshops,San Diego,2018:1-6.
|
[27] |
GAO Rui,CHENG Deqiang,YAO Jie,et al. Low-rank representation-based image super-resolution reconstruction with edge-preserving[J]. KSII Transaction on Internet and Information Systems,2020,14(9):3745-3761.
|
[28] |
JIANG He,CONG Zaichen,GAO Zhiyong,et al. Image super-resolution with facet improvement and detail enhancement based on local self examples[C]. International Conference on Wireless Communications and Signal Processing,Hangzhou,2013:1-6.
|
[29] |
JIANG He,YANG Jie. Optimized image up-scaling from learning selective similarity[C]. International Conference on Neural Information Processing,Guangzhou,2017:467-475.
|
[30] |
程德强,陈亮亮,蔡迎春,等. 边缘融合的多字典超分辨率图像重建算法[J]. 煤炭学报,2018,43(7):2084-2090.
CHENG Deqiang,CHEN Liangliang,CAI Yingchun,et al. Image super-resolution reconstruction based on multi-dictionary and edge fusion[J]. Journal of China Coal Society,2018,43(7):2084-2090.
|
[31] |
CHEN Liangliang,KOU Qiqi,CHENG Deqiang,et al. Content-guided deep residual network for single image super-resolution[J]. Optik,2020,202. DOI: 10.1016/j.ijleo.2019.163678.
|
[32] |
贾克斌,崔腾鹤,刘鹏宇,等. 基于深层特征学习的高效率视频编码中帧内快速预测算法[J]. 电子与信息学报,2021,43(7):2023-2031.
JIA Kebin,CUI Tenghe,LIU Pengyu,et al. Fast prediction algorithm in high efficiency video coding intra-mode based on deep feature learning[J]. Journal of Electronics & Information Technology,2021,43(7):2023-2031.
|
[33] |
XU Guoping,LIAO Wentao,ZHANG Xuan,et al. Haar wavelet downsampling:a simple but effective downsampling module for semantic segmentation[J]. Pattern Recognition,2023,143. DOI: 10.1016/j.patcog.2023.109819.
|
[34] |
文学志,方巍,郑钰辉. 一种基于类Haar特征和改进AdaBoost分类器的车辆识别算法[J]. 电子学报,2011,39(5):1121-1126.
WEN Xuezhi,FANG Wei,ZHENG Yuhui. An algorithm based on Haar-like features and improved AdaBoost classifier for vehicle recognition[J]. Acta Electronica Sinica,2011,39(5):1121-1126.
|
[35] |
黄威铭,吴焯标,陶铭. 基于HOG和SVM的嵌入式行人检测与追踪系统设计与实现[J]. 物联网技术,2023,13(8):29-32.
HUANG Weiming,WU Zhuobiao,TAO Ming. Design and implementation of an embedded pedestrian detection and tracking system based on HOG and SVM[J]. Internet of Things Technologies,2023,13(8):29-32.
|
[36] |
华同兴,邢存恩,赵亮. 基于Faster R−CNN的煤岩识别与煤层定位测量[J]. 矿山机械,2019,47(8):4-9.
HUA Tongxing,XING Cun'en,ZHAO Liang. Recognition of coal rock and positioning measurement of coal seam based on Faster R-CNN[J]. Mining & Processing Equipment,2019,47(8):4-9.
|
[37] |
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 and Machine Intelligence,2017,39(6). DOI: 10.1109/TPAMI.2016.2577031.
|
[38] |
BOCHKOVSKIY A,WANG C Y,LIAO H Y M . YOLOv4:optimal speed and accuracy of object detection[Z]. DOI: 10.48550/arXiv.2004.10934.
|
[39] |
郭永存,杨豚,王爽. 基于改进YOLOv4–Tiny的矿井电机车多目标实时检测[J]. 工程科学与技术,2023,55(5):232-241.
GUO Yongcun,YANG Tun,WANG Shuang. Multi-object real-time detection of mine electric locomotive based on improved YOLOv4-Tiny[J]. Advanced Engineering Sciences,2023,55(5):232-241.
|
[40] |
LIU Wei,ANGUELOV D,ERHAN D,et al. SSD:single shot multiBox detector[C]. European Conference on Computer Vision,Amsterdam,2016:21-37.
|
[41] |
葛淑伟,张永茜,秦嘉欣,等. 基于优化SSD−MobileNetV2的煤矿井下锚孔检测方法[J]. 采矿与岩层控制工程学报,2023,5(2):66-74.
GE Shuwei,ZHANG Yongqian,QIN Jiaxin,et al. Rock bolt borehole detection method for underground coal mines based on optimized SSD-MobileNetV2[J]. Journal of Mining and Strata Control Engineering,2023,5(2):66-74.
|
[42] |
张玉涛,张梦凡,史学强,等. 基于深度学习的井下运动目标跟踪算法研究[J]. 煤炭工程,2022,54(10):151-155.
ZHANG Yutao,ZHANG Mengfan,SHI Xueqiang,et al. Object tracking algorithm of moving objects in underground mine based on deep learning[J]. Coal Engineering,2022,54(10):151-155.
|
[43] |
程德强,王雨晨,寇旗旗,等. 基于改进深度残差网络的矿井图像分类[J]. 计算机应用研究,2021,38(5):1576-1580.
CHENG Deqiang,WANG Yuchen,KOU Qiqi,et al. Classification of mine images based on improved deep residual network[J]. Application Research of Computers,2021,38(5):1576-1580.
|
[44] |
邵小强,李鑫,杨涛,等. 改进YOLOv5s和DeepSORT的井下人员检测及跟踪算法[J/OL]. 煤炭科学技术:1-12 [2023-08-31]. https://doi.org/10.13199/j.cnki.cst.2022-1933.
SHAO Xiaoqiang,LI Xin,YANG Tao,et al. Improving underground personnel eetection and tracking algorithms for YOLOv5s and DeepSORT [J/OL]. Coal Science and Technology:1-12 [2023-08-31]. https://doi.org/10.13199/j.cnki.cst.2022-1933.
|
[45] |
JACQUES G,NOURELDIN A,MAHMOUD E,et al. Wellbore surveying while drilling based on Kalman filtering[J]. American Journal of Engineering and Applied Sciences,2010,3(2):240-259. doi: 10.3844/ajeassp.2010.240.259
|
[46] |
崔丽珍,张清宇,郭倩倩,等. 基于CNN−LSTM的井下人员行为模式识别模型[J]. 无线电工程,2023,53(6):1375-1381.
CUI Lizhen,ZHANG Qingyu,GUO Qianqian,et al. Underground personnel behavior pattern recognition model based on CNN-LSTM[J]. Radio Engineering,2023,53(6):1375-1381.
|
[47] |
李尧,桂方俊. 基于EfficientNet和LSTM的井下设备定位技术[J]. 信息记录材料,2023,24(5):121-123,126.
LI Yao,GUI Fangjun. Underground equipment positioning technology based on EfficientNet and LSTM[J]. Information Recording Materials,2023,24(5):121-123,126.
|
[48] |
党伟超,张泽杰,白尚旺,等. 基于改进双流法的井下配电室巡检行为识别[J]. 工矿自动化,2020,46(4):75-80.
DANG Weichao,ZHANG Zejie,BAI Shangwang,et al. Inspection behavior recognition of underground power distribution room based on improved two-stream CNN method[J]. Industry and Mine Automation,2020,46(4):75-80.
|
[49] |
程德强,刘洁,郭政. 基于分层光流的煤矿井下运动目标跟踪算法[J]. 工矿自动化,2015,41(3):75-79.
CHENG Deqiang,LIU Jie,GUO Zheng. An algorithm for moving targets tracking in coal mine underground based on layered optical flow[J]. Industry and Mine Automation,2015,41(3):75-79.
|
[50] |
YE Xuehong,XIAO Qingwei. Research on target tracking in coal mine based on optical flow method[C]. International Conference on Graphic and Image Processing,Suzhou,2015:1-6.
|
[51] |
刘江,郭荣春,王燕妮. 基于卡尔曼滤波的高斯混合模型目标检测算法[J]. 探测与控制学报,2022,44(2):79-84.
LIU Jiang,GUO Rongchun,WANG Yanni. A gaussian mixture Kalman filter algorithm of target detection[J]. Journal of Detection & Control,2022,44(2):79-84.
|
[52] |
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.
|
[53] |
刘伟,郝晓丽,吕进来. 自适应混合高斯建模的高效运动目标检测[J]. 中国图象图形学报,2020,25(1):113-125.
LIU Wei,HAO Xiaoli,LYU Jinlai. Efficient moving targets detection based on adaptive Gaussian mixture modelling[J]. Journal of Image and Graphics,2020,25(1):113-125.
|
[54] |
申柯,陈熙. 增强型局部二值模式及其图像纹理特征提取[J]. 计算机仿真,2023,40(6):260-267,326.
SHEN Ke,CHEN Xi. Enhanced local binary pattern and its image texture feature extraction[J]. Computer Simulation,2023,40(6):260-267,326.
|
[55] |
JIANG He,GAO Zhiyong,ZHANG Xiaoyun. Image super resolution based on local self examples with nonlocal constraints and enhancement with 2-order holomorphic complete differential kernel[C]. International Conference on Audio,Language and Image Processing,Shanghai,2014:759-764.
|
[56] |
程德强,张皓翔,江曼,等. 融合主曲率与颜色信息的彩色图像检索算法[J]. 计算机辅助设计与图形学学报,2021,33(2):223-231.
CHENG Deqiang,ZHANG Haoxiang,JIANG Man,et al. Color image retrieval method fusing principal curvature and color information[J]. Journal of Computer-Aided Design & Computer Graphics,2021,33(2):223-231.
|
[57] |
ZHANG Haoxiang,JINAG Man,KOU Qiqi. Color image retrieval algorithm fusing color and principal curvatures information[J]. IEEE Access,2020(8):184945-184954.
|
[58] |
江曼,张皓翔,程德强,等. 融合HSV与方向梯度特征的多尺度图像检索[J]. 光电工程,2021,48(11):64-76.
JIANG Man,ZHANG Haoxiang,CHENG Deqiang,et al. Multi-scale image retrieval based on HSV and directional gradient features[J]. Opto-Electronic Engineering,2021,48(11):64-76.
|
[59] |
寇旗旗,程德强,于文洁,等. 一种基于颜色和纹理信息的运动目标识别装置及方法:CN110232703A[P]. 2019-09-13.
KOU Qiqi,CHENG Deqiang,YU Wenjie,et al. A moving target recognition device and method based on color and texture information:CN110232703A[P]. 2019-09-13.
|
[60] |
XUE Yuting,BAHRAMI D,ZHOU Lihong. Identifying the location and size of an underground mine fire with simulated ventilation data and random forest model[J]. Mining,Metallurgy & Exploration,2023,40(4):1399-1407.
|
[61] |
万航. 煤矿井下视频监控异常行为识别算法的研究[D]. 太原:太原科技大学,2013.
WAN Hang. Research on abnormal behavior recognition algorithm for video surveillance in coal mines [D]. Taiyuan:Taiyuan University of Science and Technology,2013.
|
[62] |
李占利,权锦成,靳红梅. 基于3D−Attention与多尺度的矿井人员行为识别算法[J]. 国外电子测量技术,2023,42(7):95-104.
LI Zhanli,QUAN Jincheng,JIN Hongmei. Mine personnel behavior recognition algorithm based on 3D-Attention and multi-scale[J]. Foreign Electronic Measurement Technology,2023,42(7):95-104.
|
[63] |
游青山,冉霞. 基于机器视觉的矿井作业人员行为监测及违章识别系统[J]. 自动化与信息工程,2021,42(4):20-24.
YOU Qingshan,RAN Xia. Behavior monitoring and violation recognition system of mine operators based on machine vision[J]. Automation & Information Engineering,2021,42(4):20-24.
|
[64] |
张立亚. 基于动目标特征提取的矿井目标监测[J]. 煤炭学报,2017,42(增刊2):603-610.
ZHANG Liya. Mine target monitoring based on dynamic target feature extraction[J]. Journal of China Coal Society,2017,42(S2):603-610.
|
[65] |
崔丽珍,吴迪,赫佳星,等. 基于改进粒子滤波的井下跟踪算法研究与实现[J]. 计算机应用研究,2017,34(5):1476-1479.
CUI Lizhen,WU Di,HE Jiaxing,et al. Research and implementation on underground tracking algorithm based on improved particle filter[J]. Application Research of Computers,2017,34(5):1476-1479.
|
[66] |
张瑞,李其申,储珺. 基于3D卷积神经网络的人体动作识别算法[J]. 计算机工程,2019,45(1):259-263.
ZHANG Rui,LI Qishen,CHU Jun. Human action recognition algorithm based on 3D convolution neural network[J]. Computer Engineering,2019,45(1):259-263.
|
[67] |
魏英姿,曹振林. 安防视频行人异常徘徊提示的决策树方法[J]. 物联网技术,2023,13(6):28-32.
WEI Yingzi,CAO Zhenlin. Decision tree method for warning pedestrian abnormal wandering in security video[J]. Internet of Things Technology,2023,13(6):28-32.
|
[68] |
刘西想. 基于机器视觉的矿井下异常行为识别研究[D]. 徐州:中国矿业大学,2021.
LIU Xixiang. Research on recognition of abnormal behavior in underground mine based on machine vision[D]. Xuzhou:China University of Mining and Technology,2021.
|
[69] |
姜珊. 基于深度学习的行为异常检测[J]. 信息技术与信息化,2021(2):216-217.
JIANG Shan. Behavioral anomaly detection based on deep learning[J]. Information Technology and Informatization,2021(2):216-217.
|
[70] |
程德强,徐进洋,寇旗旗,等. 融合残差信息轻量级网络的运煤皮带异物分类[J]. 煤炭学报,2022,47(3):1361-1369.
CHENG Deqiang,XU Jinyang,KOU Qiqi,et al. Lightweight network based on residual information for foreign body classification on coal conveyor belt[J]. Journal of China Coal Society,2022,47(3):1361-1369.
|
[71] |
黄瀚,程小舟,云霄,等. 基于DA−GCN的煤矿人员行为识别方法[J]. 工矿自动化,2021,47(4):62-66.
HUANG Han,CHENG Xiaozhou,YUN Xiao,et al. DA-GCN-based coal mine personnel action recognition method[J]. Industry and Mine Automation,2021,47(4):62-66.
|
[72] |
ASAD M,JIANG He,YANG Jie,et al. Multi-level two-stream fusion-based spatio-temporal attention model for violence detection and localization[J]. International Journal of Pattern Recognition and Artificial Intelligence,2022,36(1). DOI:10.1142/ S0218001422550023.
|
[73] |
ASAD M J,JIANG He,YANG Jie,et al. Multi-stream 3D latent feature clustering for abnormality detection in videos[J]. Applied Intelligence,2021,52:1126-1143.
|
[74] |
王伟峰,张宝宝,王志强,等. 基于YOLOv5的矿井火灾视频图像智能识别方法[J]. 工矿自动化,2021,47(9):53-57.
WANG Weifeng,ZHANG Baobao,WANG Zhiqiang,et al. Intelligent identification method of mine fire video images based on YOLOv5[J]. Industry and Mine Automation,2021,47(9):53-57.
|
[75] |
YANG Jianping,PENG Jianlin,LI Yida,et al. Gangue localization and volume measurement based on adaptive deep feature fusion and surface curvature filter[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-13.
|
[76] |
许鹏. 基于边缘计算的煤矿井下皮带异物检测关键技术研究[D]. 徐州:中国矿业大学,2021.
XU Peng. Study on the key technology of foreign object detection of coal mine belt based on edge computing[D]. Xuzhou:China University of Mining and Technology,2021.
|
[77] |
胡璟皓. 基于深度学习的带式输送机非煤异物视频检测系统[D]. 太原:太原理工大学,2021.
HU Jinghao. Video monitoring system for non-coal foreign matter of belt conveyor based on deep learning[D]. Taiyuan:Taiyuan University of Technology,2021.
|
[78] |
程德强,郭昕,陈亮亮,等. 多通道递归残差网络的图像超分辨率重建[J]. 中国图象图形学报,2021,26(3):605-618.
CHENG Deqiang,GUO Xin,CHEN Liangliang,et al. Image super-resolution reconstruction from multi-channel recursive residual network[J]. Journal of Image and Graphics,2021,26(3):605-618.
|
[79] |
郝帅,张旭,马旭,等. 基于CBAM−YOLOv5的煤矿输送带异物检测[J]. 煤炭学报,2022,47(11):4147-4156.
HAO Shuai,ZHANG Xu,MA Xu,et al. Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5[J]. Journal of China Coal Society,2022,47(11):4147-4156.
|
[80] |
刘浩,刘海滨,孙宇,等. 煤矿井下员工不安全行为智能识别系统[J]. 煤炭学报,2021,46(增刊2):1159-1169.
LIU Hao,LIU Haibin,SUN Yu,et al. Intelligent recognition system of unsafe behavior of underground coal miners[J]. Journal of China Coal Society,2021,46(S2):1159-1169.
|
[81] |
张立亚. 矿山智能视频分析与预警系统研究[J]. 工矿自动化,2017,43(11):16-20.
ZHANG Liya. Research on intelligent video analysis and early warning system for mine[J]. Industry and Mine Automation,2017,43(11):16-20.
|
[82] |
屈世甲,武福生. 基于边缘计算的采煤工作面甲烷监测模式研究[J]. 煤炭科学技术,2020,48(12):161-167.
QU Shijia,WU Fusheng. Research on methane monitoring mode of coal mining face based on edge computing[J]. Coal Science and Technology,2020,48(12):161-167.
|
[83] |
HESCOCK J,NEWMAN C,AGIOUTANTIS Z. Development of a new algorithm for implementing the edge effect offset for subsidence calculations[J]. International Journal of Mining Science and Technology,2018,28(1):61-66. doi: 10.1016/j.ijmst.2017.11.010
|
[84] |
朱晓娟,张浩. 智慧煤矿中边缘计算任务分配研究[J]. 工矿自动化,2021,47(6):32-39.
ZHU Xiaojuan,ZHANG Hao. Research on task allocation of edge computing in intelligent coal mine[J]. Industry and Mine Automation,2021,47(6):32-39.
|
[85] |
李敬兆,秦晓伟,汪磊. 基于边云协同框架的煤矿井下实时视频处理系统[J]. 工矿自动化,2021,47(12):1-7.
LI Jingzhao,QIN Xiaowei,WANG Lei. Real-time video processing system in coal mine based on edge-cloud collaborative framework[J]. Industry and Mine Automation,2021,47(12):1-7.
|
[86] |
屈世甲,武福生,贺耀宜. 边缘计算模式在煤矿监测监控体系中的应用[J]. 煤炭科学技术,2021,50(5):1-8.
QU Shijia,WU Fusheng,HE Yaoyi. Application of edge computing mode in coal mine monitoring and control system[J]. Coal Science and Technology,2021,50(5):1-8.
|
[87] |
牟琦,韩嘉嘉,张寒,等. 基于云边协同的煤矿井下尺度自适应目标跟踪方法[J]. 工矿自动化,2023,49(4):50-61.
MU Qi,HAN Jiajia,ZHANG Han,et al. A scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration[J]. Journal of Mine Automation,2023,49(4):50-61.
|
[88] |
程德强,钱建生,郭星歌,等. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术,2023,51(2):349-365.
CHENG Deqiang,QIAN Jiansheng,GUO Xingge,et al. Review on key technologies of AI recognition for videos in coal mine[J]. Coal Science and Technology,2023,51(2):349-365.
|