Architecture and key technologies of coalmine underground vision computing
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摘要:
煤矿井下特别是采掘工作面空间狭窄、装备众多、工艺条件及环境复杂、隐蔽致灾隐患多，因此实现智能化无人操作一直是煤炭行业内的普遍需求。建立有效的面向煤矿井下应用的视觉计算理论是实现煤矿智能化无人开采的重要一环。矿井视觉计算的主要任务是针对矿井这一特定应用领域，研究煤矿井下环境的感知、描述、识别和理解模型与框架，以使智能装备具有通过图像或视频感知煤矿井下三维环境信息，增强煤矿井下环境感知能力。为了有效推进该理论与实践的结合发展，使其更好地服务于煤矿智能化建设，首先围绕煤矿井下视觉计算的基本概念，分析计算机视觉与矿井视觉计算的异同，总结提出煤矿井下视觉计算的组成架构体系。然后，详细介绍煤矿井下视觉计算所涉及的视觉感知与增强、特征提取与特征描述、语义学习与视觉理解、三维视觉与空间重建、感算一体与边缘智能等关键技术，并从矿井视频智能识别、预警与机器人定位、导航等方面简要介绍视觉计算在煤矿井下的典型应用案例。最后给出煤矿井下视觉计算的发展趋势和展望，重点总结分析了目前矿井视觉计算在煤矿井下应用中存在的关键技术难题和矿井增强现实/混合现实、平行智能采矿2种重要的发展方向。随着煤矿井下视觉计算理论的不断突破和完善，矿井视觉计算在煤矿智能化发展中必将发挥越来越重要的作用。
Abstract:It has always been a common demand to stay away from the harsh environment with narrow space, numerous devices, complex operation process, and hidden hazards, and realize intelligent unmanned mining in the coal industry. To achieve this goal, it is very necessary for us to develop an effective theory of vision computing for underground coalmine applications. Its main task is to build effective models or frameworks for perceiving, describing, recognizing and understanding the environment of underground coalmine, and let intelligent equipment get 3D environment information in coalmine from images or videos. To effectively develop this theory and make it better for intelligent development of coalmine, this paper first analyzed the similarities and differences about computer vision and visual computing in coalmine, and proposed its composition architecture. And then, this paper introduced in detail the key technologies involved in visual computing in coalmine including visual perception and light field computing, feature extraction and feature description, semantic learning and vision understanding, 3D vision reconstruction, and sense computing integration and edge intelligence, which is followed by typical application cases of visual computing in coalmines. Finally, the development trend and prospect of underground visual computing in coalmine was given. In this section, this paper focused on concluding the key challenges and introducing two valuable applications including coalmine Augmented Reality/Mixed Reality and parallel intelligent mining. With the breakthrough of underground vision computing, it will play a more and more important role in the intelligent development of coal mines.
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图 6 感算一体与边缘智能架构
Figure 6. Architecture of sense computing integration and edge intelligence
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图 10 矿井视觉计算增强临场感技术路线
Figure 10. Technical route of mine visual computing to enhance presence
表 1 煤矿视频智能识别系统典型应用
Table 1 Typical applications of video intelligent identification system in coal mine
生产场景 典型应用 功能描述 综采系统 护帮检测 监测工作面护帮板打开关闭、片帮状态，让机器及时感知护帮板的状态，能够检测设备是否正常运转 断链识别 监测刮板输送机的圆环链是否处在正常运行状态，一旦发生断链，立刻识别并报警 滚筒识别 实时监测割煤机前后滚筒位置、滚筒高度、运动方向，让机器及时感知滚筒的状态，能够检测设备是否
正常运转大煤识别 刮板输送机大块煤识别及告警，监测是否处在正常运行状态 掘进系统 危险监测 综掘机处于开机运行状态时，系统识别到人员进入危险区域内时，声光报警器报警并停机，提示人员离开 人员统计 对整个掘进工作面所有区域的联动跟机分析判断掘进面现有人员的数量和实时的位置 堆煤识别 整个掘进工作面如果产生了大量的煤炭堆积在一起，导致掘进系统发生困难，立刻报警 钻机识别 从锚杆钻机工作状态判断当前是否在进行打锚杆操作，对锚杆钻机主要使用模型匹配方法进行识别 运输系统 输送带跑偏 识别输送带和滚轮的左右边距变化来实时判断输送带是否有跑偏行为，一旦发生跑偏行为，给予提示纠正 水煤检测 检测运输的煤炭是否有水煤，因为潮湿的煤炭不符合生产质量，严重影响煤炭的经济价值 煤量统计 根据输送带实时载物百分比通过识别输送带上煤量和速度，利用机器学习的后台算法对输送带
煤量实时识别煤矸识别 煤矸石是一种含碳量较低，比煤坚硬的岩石，煤矸石降低原煤的纯度，从而影响原煤的经济价值 提升系统 尾绳监测 检测尾绳运行状态，对摆动异常、尾绳散股、尾绳缠绕、尾绳脱落等异常进行报警 断裂识别 自动检测、判断钢丝绳变形情况（钢丝绳直径局部变大、局部变小、钢丝绳局部变形或损伤等） 首绳检测 提升机全速运行期间，全程实时检测每根钢丝绳直径、捻距、断丝、变形、磨损等外观缺陷 箕斗残留 箕斗残留检测，实时检测提升箕斗卸载的残留状态，卸载残留达到设定的阈值自动报警 洗选系统 输送带调速 通过向PLC变频器发控制指令从而实现对输送带的五档智能调速，以达到节能降耗、减少设备磨损目的 超温监测 如果生产设备的超负荷运转，温度超过一定的阈值，会严重影响设备寿命，甚至引起火灾 非法侵入 当出现人员违规进入危险区域时，当遇到入侵、跨越、逗留等违规行为时，可进行告警，提醒人员离开 异物识别 对于分选系统中有锚杆、铁丝网、木块等非装载异物进行智能识别 
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表 2 煤矿视频智能预警系统典型应用
Table 2 Typical application of video intelligent warning platform in coal mine
生产场景 典型应用 功能描述 关键岗位 脱岗监测 在关键岗位，检查工作人员是否脱离工作岗位，违反工作纪律，给生产造成重大的安全事故隐患 睡岗检测 在关键岗位，检查工作人员是否睡觉，违反工作纪律，给生产造成重大的安全事故隐患 定期巡检 在关键设备岗位，需要巡检人员定期检查设备的工作状况，通过摄像头感知人员是否按照时间到指定的场所检查设备 姿态识别 设计摄像头人员姿态估计识别算法，感知矿工的动作异常行为分析，比如下蹲、坐卧等异常姿态分析 人员管理 戴安全帽 在矿区场景中，如果不带安全帽，会严重影响个人的人身安全，因此设计识别矿工是否佩戴安全帽算法 穿工作服 在矿区场景中，要统一穿工作服，不然会严重影响个人人身安全，因此设计识别矿工是否穿工作服进行施工 非法闯入 对监控区域进行7×24 h全天候管控，当监测到有人员靠近、闯入时，立即报警及时通知安全管理人员及时处理 人员定位 在井上井下场景中，需要了解每一个井下人员位置信息，以方便管理，因此设计摄像头算法定位矿工的地理位置信息 特定场所 积水检测 在水泵房中，如果出现大面积的漏水或者积水，会威胁或影响生产，因此建立积水预警系统 吸烟检测 在矿区中，吸烟容易引起爆炸，从而威胁人员安全或影响生产，因此建立吸烟预警系统 打架检测 在特定场景中，比如综采工作面或掘进工作面，为防止矿工打架斗殴威胁人员的安全或影响生产，建立打架预警系统 摔倒识别 在特定场景中，比如综采工作面或掘进工作面，为防止人员意外摔倒威胁人员的安全或影响生产，建立摔倒预警系统 入口出口 人数统计 在矿区出入口管理中，为控制入井的人数，在入口出口设置人数统计算法，发生拥塞，立刻报警 拥堵检测 在矿区出入口管理中，为防止交通堵塞或者人员拥堵，在入口出口设置拥堵检测算法，发生堵塞，立刻报警 人脸识别 为了方便矿区人员的管理考勤，例如准时上下班，在关键的矿井出入口设置人脸识别系统，可以考察人员考勤状态 超限识别 在矿区车辆装载货物时候，如果货物超出一定的高度，宽度，长度或者重量的时候，严重威胁矿里的安全系统要报警 车辆管理 车牌识别 在能够识别进入井上和井下每一辆的车牌信息，以便了解矿山车辆的调度信息和运行状态，为车辆运维提供全面信息 车辆逆行 在有车辆巷道里面，为便于管理井下的交通，有些道路不允许逆行车辆，一旦发现车辆逆行，进行报警 车辆违停 在井下高危险作业基地，为便于管理井下的交通，危险的区域不允许车辆停放，发现车辆停放，立刻进行警 超速识别 在有车辆巷道里面，为避免交通事故，车辆不允许超速，发现车辆超速，进行报警
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[1] 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. doi: 10.13225/j.cnki.jccs.2018.5034 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. doi: 10.13225/j.cnki.jccs.2018.5034
[2] 王国法,王 虹,任怀伟,等. 智慧煤矿2025情景目标和发展路径[J]. 煤炭学报,2018,43(2):295−305. doi: 10.13225/j.cnki.jccs.2018.0152 WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society,2018,43(2):295−305. doi: 10.13225/j.cnki.jccs.2018.0152
[3] 范京道,徐建军,张玉良,等. 不同煤层地质条件下智能化无人综采技术[J]. 煤炭科学技术,2019,47(3):43−52. doi: 10.13199/j.cnki.cst.2019.03.006 FAN Jingdao,XU Jianjun,ZHANG Yuliang,et al. Intelligent unmanned fully-mechanized mining technology under conditions of different seams geology[J]. Coal Science and Technology,2019,47(3):43−52. doi: 10.13199/j.cnki.cst.2019.03.006
[4] 王家臣,PENG Syd S,李 杨. 美国煤炭地下开采与自动化技术发展[J]. 煤炭学报,2021,46(1):36−45. WANG Jiachen,PENG Syd S,LI Yang. State-of-the-art in underground coal mining and automation technology in the United States[J]. Journal of China Coal Society,2021,46(1):36−45.
[5] 王国法,赵国瑞,胡亚辉. 5G技术在煤矿智能化中的应用展望[J]. 煤炭学报,2020,45(1):16−23. doi: 10.13225/j.cnki.jccs.YG19.1515 WANG Guofa,ZHAO Guorui,HU Yahui. Application prospect of 5G technology in coal mine intelligence[J]. Journal of China Coal Society,2020,45(1):16−23. doi: 10.13225/j.cnki.jccs.YG19.1515
[6] 李首滨. 煤炭智能化无人开采的现状与展望[J]. 中国煤炭,2019,45(4):5−12. doi: 10.3969/j.issn.1006-530X.2019.04.001 LI Shoubin. Present situation and prospect on intelligent unmanned mining at work face[J]. China Coal,2019,45(4):5−12. doi: 10.3969/j.issn.1006-530X.2019.04.001
[7] CHENG J, GANG Y, BAI S, et al. An Improved GMM-based moving object detection method under sudden illumination change[J]. Communications in Computer and Information Science. 2018, 952: 178-187.
[8] MARR D. Vision: A computational Investigation into the human representation and processing of visual information[M]. W. H. Freeman and Company, 1982.
[9] 马颂德, 张正友. 计算机视觉 : 计算理论与算法基础[M]. 北京: 科学出版社, 1998. [10] 谢剑斌. 视觉计算方法[M]. 北京: 科学出版社, 2017. [11] 胡占义. 计算机视觉: 历史、现状和发展趋势[EB/OL]. https://blog.csdn.net/qq_28168421/article/details/82835966, 2023-08-29. [12] 陈 龙,王 晓,杨健健,等. 平行矿山: 从数字孪生到矿山智能[J]. 自动化学报,2021,47(7):1633−1645. CHEN Long,WANG Xiao,YANG Jianjian,et al. Parallel mining operating systems: from digital twins to mining intelligence[J]. Acta Automation Sinica,2021,47(7):1633−1645.
[13] 智 宁,毛善君,李 梅. 基于照度调整的矿井非均匀照度视频图像增强算法[J]. 煤炭学报,2017,42(8):2190−2197. doi: 10.13225/j.cnki.jccs.2017.0048 ZHI Ning,MAO Shanjun,LI Mei. Enhancement algorithm based on illumination adjustment for non-uniform illumination video images in coal mine[J]. Journal of China Coal Society,2017,42(8):2190−2197. doi: 10.13225/j.cnki.jccs.2017.0048
[14] 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. doi: 10.13225/j.cnki.jccs.2020.0785 FAN Weiqiang,LIU Yi. Fuzzy enhancement algorithm of coal mine degradation image based on adaptive wavelet trans form[J]. Journal of China Coal Society,2020,45(12):4248−4260. doi: 10.13225/j.cnki.jccs.2020.0785
[15] 吴佳奇,张文琪,陈 伟,等. 基于改进CycleGAN的煤矿井下低照度图像增强方法[J]. 华中科技大学学报(自然科学版),2023,51(5):40−46. doi: 10.13245/j.hust.229323 WU Jiaqi,ZHANG Wenqi,CHEN Wei,et al. Image enhancement method of low illumination in coal mine based on improved CycleGAN[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition),2023,51(5):40−46. doi: 10.13245/j.hust.229323
[16] 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(S1):532−540. doi: 10.13225/j.cnki.jccs.2020.0514 ZHANG Liya,HAO Bonan,MENG Qingyong,et al. Method of image enhancement in coal mine based on improved re tex fusion algorithm in HSV space[J]. Journal of China Coal Society,2020,45(S1):532−540. doi: 10.13225/j.cnki.jccs.2020.0514
[17] WEI C, WANG W, YANG W, et al. Deep Retinex decomposition for low-light enhancement[C] // Proceedings of the the British Machine Vision Conference. Newcastle, UK: Springer, 2018.
[18] LIU R, MA L, ZHANG J, et al. Retinex-inspired unrolling with cooperation prior architecture search for low-light image enhancement[C] // Proceedings of the IEEE conference on computer vision and pattern recognition. Virtual: IEEE, 2021.
[19] ZHANG X, GUO H. Research on an improved algorithm for image dehazing in underground coal mine[C] //Proceedings of the international conference on computer information science and artificial intelligence. Hulun Buir, China: IOP Publishing, 2020.
[20] 程 健,闫鹏鹏,郁华森,等. 基于有向线段误匹配剔除的煤矿巷道复杂场景图像拼接方法[J]. 煤炭科学技术,2022,50(9):179−191. doi: 10.13199/j.cnki.cst.2021-0219 CHENG Jian,YAN Pengpeng,YU Huasen,et al. Image stitching method for the complicated scene of coalmine tunnel based on mismatched elimination with directed line segments[J]. Coal Science and Technology,2022,50(9):179−191. doi: 10.13199/j.cnki.cst.2021-0219
[21] XU Z,YANG W,YOU K,et al. Vehicle autonomous localization in local area of coal mine tunnel based on vision sensors and ultrasonic sensors[J]. PLoS ONE,2017,12(1):1−31.
[22] 杨文娟,张旭辉,张 超,等. 基于三激光束标靶的煤矿井下长距离视觉定位方法[J]. 煤炭学报,2022,47(2):986−1001. doi: 10.13225/j.cnki.jccs.xr21.1762 YANG Wenjuan,ZHANG Xuhui,ZHANG Chao,et al. Long distance vision localization method based on triple laser beams target in coal mine[J]. Journal of China Coal Society,2022,47(2):986−1001. doi: 10.13225/j.cnki.jccs.xr21.1762
[23] 陈先中,刘荣杰,张 森,等. 煤矿地下毫米波雷达点云成像与环境地图导航研究进展[J]. 煤炭学报,2020,45(6):2182−2192. doi: 10.13225/j.cnki.jccs.zn20.0316 CHEN Xianzhong,LIU Rongjie,ZHANG Sen,et al. Development of millimeter wave radar imaging and SLAM in underground coal mine environment[J]. Journal of China Coal Society,2020,45(6):2182−2192. doi: 10.13225/j.cnki.jccs.zn20.0316
[24] 周李兵. 煤矿井下无轨胶轮车无人驾驶系统研究[J]. 工矿自动化,2022,48(6):36−48. ZHOU Libing. Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine[J]. Journal of Mine Automation,2022,48(6):36−48.
[25] DERPANIS K G. The harris corner detector[J]. York University,2004,2:1−2.
[26] TRAJKOVIĆ M,HEDLEY M. Fast corner detection[J]. Image and vision computing,1998,16(2):75−87. doi: 10.1016/S0262-8856(97)00056-5
[27] CALONDER M, LEPETIT V, STRECHA C, et al. Brief: Binary robust independent elementary features[C] //Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, 2010: 778−792.
[28] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C] //2011 International conference on computer vision. IEEE, 2011: 2564−2571.
[29] LOWE D G. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision,2004,60(2):91−110. doi: 10.1023/B:VISI.0000029664.99615.94
[30] BAY H,ESS A,TUYTELAARS T,et al. Speeded-up robust features (SURF)[J]. Computer vision and image understanding,2008,110(3):346−359. doi: 10.1016/j.cviu.2007.09.014
[31] MOREL J M,YU G. ASIFT: a new framework for fully affine invariant image comparison[J]. SIAM Journal on Imaging Sciences,2009,2(2):438−469. doi: 10.1137/080732730
[32] MIKOLAJCZYK K, SCHMID C. An affine invariant interest point detector[C] //Proceedings of the European Conference on Computer Vision. Copenhagen, Denmark: Springer, 2002.
[33] MATAS J,CHUM O,URBAN M,et al. Robust wide-baseline stereo from maximally stable extremal regions[J]. Image and Vision Computing,2004,22(10):761−767. doi: 10.1016/j.imavis.2004.02.006
[34] DUDA R O,HART P E. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM,1972,15(1):11−15. doi: 10.1145/361237.361242
[35] MATAS J,GALAMBOS C,KITTLER J. Robust detection of lines using the progressive probabilistic hough transform[J]. Computer vision and image understanding,2000,78(1):119−137. doi: 10.1006/cviu.1999.0831
[36] VON GIOI R G,JAKUBOWICZ J,MOREL J M,et al. LSD: A fast line segment detector with a false detection control[J]. IEEE transactions on pattern analysis and machine intelligence,2008,32(4):722−732.
[37] AKINLAR C,TOPAL C. EDLines: A real-time line segment detector with a false detection control[J]. Pattern Recognition Letters,2011,32(13):1633−1642. doi: 10.1016/j.patrec.2011.06.001
[38] GU G, KO B, GO S H, et al. Towards light-weight and real-time line segment detection[C] //Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(1): 726−734.
[39] DAI X,GONG H,WU S,et al. Fully convolutional line parsing[J]. Neurocomputing,2022,506:1−11. doi: 10.1016/j.neucom.2022.07.026
[40] ZHOU Y, QI H, MA Y. End-to-end wireframe parsing[C] //Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 962−971.
[41] LIN Y, PINTEA S L, VAN GEMERT J C. Deep hough-transform line priors[C] //Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16. Springer International Publishing, 2020: 323−340.
[42] BLOESCH M, CZARNOWSKI J, CLARK R, et al. CodeSLAM—learning a compact, optimizable representation for dense visual SLAM[C] //Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560−2568.
[43] LIU Y,GUO C,WANG Y. Object-aware data association for the semantically constrained visual SLAM[J]. Intelligent Service Robotics,2023,16(2):155−176. doi: 10.1007/s11370-023-00455-9
[44] 程 健,王东伟,杨凌凯,等. 一种改进的高斯混合模型煤矸石视频检测方法[J]. 中南大学学报(自然科学版),2018,49(1):118−123. CHENG Jian,WANG Dongwei,YANG Lingkai,et al. An improved gaussian mixture model for coal gangue video detection[J]. Journal of Central South University (Science ang Technology),2018,49(1):118−123.
[45] HE K, GKIOXARI G, DOLLÁR P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961−2969.
[46] CAI Z,VASCONCELOS N. Cascade R-CNN: high quality object detection and instance segmentation[J]. IEEE transactions on pattern analysis and machine intelligence,2019,43(5):1483−1498.
[47] WANG T, XIONG J, XU X, et al. SCNN: A general distribution based statistical convolutional neural network with application to video object detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(1): 5321−5328.
[48] FAN Q, ZHUO W, TANG C K, et al. Few-shot object detection with attention-RPN and multi-relation detector[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 4013−4022.
[49] ZHU X, SU W, LU L, et al. Deformable detr: Deformable transformers for end-to-end object detection[J]. arXiv preprint arXiv: 2010.04159, 2020.
[50] FAN D P,JI G P,CHENG M M,et al. Concealed object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44(10):6024−6042.
[51] DI L, DAI J, JIA J, et al. ScribbleSup: scribble-supervised convolutional networks for semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016.
[52] DAI J, HE K, SUN J. BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation[C] //Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015.
[53] BEARMAN A, RUSSAKOVSKY O, FERRARI V, et al. What's the Point: Semantic Segmentation with Point Supervision[C]//Proceedings of the European Conference on Computer Vision. Amsterdam, Netherlands: Springer, 2016.
[54] HONG Y, PAN H, SUN W, et al. Representation separation for semantic segmentation with vision transformers[J]. arXiv preprint arXiv: 2212.13764, 2022.
[55] 温廷新,王贵通,孔祥博,等. 基于迁移学习与残差网络的矿工不安全行为识别[J]. 中国安全科学学报,2020,30(3):41−46. doi: 10.16265/j.cnki.issn1003-3033.2020.03.007 WEN Tingxin,WANG Guitong,KONG Xiangbo,et al. Identification of miners’unsafe behaviors based on transfer learning and residual network[J]. China Safety Science Juurnal,2020,30(3):41−46. doi: 10.16265/j.cnki.issn1003-3033.2020.03.007
[56] 鞠春雷,聂方超,刘文岗,等. 基于长短期记忆网络的矿工不安全行为研究[J]. 煤矿安全,2020,51(9):260−264. doi: 10.13347/j.cnki.mkaq.2020.09.054 JU Chunlei,NIE Fangchao,LIU Wengang,et al. Research on miner’s unsafe behavior based on long and short term memory[J]. Safety in Coal Mines,2020,51(9):260−264. doi: 10.13347/j.cnki.mkaq.2020.09.054
[57] CHEN P,GAN C,SHEN G,et al. Relation attention for temporal action localization[J]. IEEE Transactions on Multimedia,2019,22(10):2723−2733.
[58] 程 健,陈 亮,王 凯,等. 一种多特征融合的复杂场景动态目标跟踪算法[J]. 中国矿业大学学报,2021,50(5):1002−1010. doi: 10.13247/j.cnki.jcumt.001333 CHENG Jian,CHEN Liang,WANG Kai,et al. Multi-feature fusion dynamic target tracking algorithm for complex scenes[J]. Journal of China University of Mining & Technology,2021,50(5):1002−1010. doi: 10.13247/j.cnki.jcumt.001333
[59] LIU X,WANG Q,HU Y,et al. End-to-end temporal action detection with transformer[J]. IEEE Transactions on Image Processing,2022,31:5427−5441. doi: 10.1109/TIP.2022.3195321
[60] ORDONEZ V, KULKARNI G, BERG T. Im2text: Describing images using 1 million captioned photographs[C] //Proceedings of the Conference on Neural Information Processing Systems. Granada, Spain: Curran Associates Inc, 2011.
[61] HAYS J, EFROS A A. IM2GPS: estimating geographic information from a single image[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008.
[62] 程 健,叶 亮,郭一楠,等. 采空区地裂缝混合域注意力变形卷积网络检测方法[J]. 煤炭学报,2020,45(S2):993−1002. doi: 10.13225/j.cnki.jccs.2020.0164 CHENG Jian,YE Liang,GUO Yinan,et al. An aerial image detection method of ground crack in goaf based on deformable convolutional network with hybrid domain attention[J]. Journal of China Coal Society,2020,45(S2):993−1002. doi: 10.13225/j.cnki.jccs.2020.0164
[63] GALLAGHER L, KUMAR V R, YOGAMANI S, et al. A hybrid sparse-dense monocular slam system for autonomous driving[C] //Proceedings of the european conference on mobile robots. Bonn, Germany: IEEE, 2021.
[64] 李首滨,李 森,张守祥,等. 综采工作面智能感知与智能控制关键技术与应用[J]. 煤炭科学技术,2021,49(4):28−39. doi: 10.13199/j.cnki.cst.2021.04.004 LI Shoubin,LI Sen,ZHANG Shouxiang,et al. Key technology and application of intelligent perception and intelligent control in fully mechanized mining face[J]. Coal Science and Technology,2021,49(4):28−39. doi: 10.13199/j.cnki.cst.2021.04.004
[65] GAO X, ZHANG T. Introduction to visual SLAM: from theory to practice[M]. Springer, 2021.
[66] HUANG K, LI J, LIU Y, et al. A survey on feature point extraction techniques[C] //Proceedings of the International SoC Design Conference. Jeju Island, Korea: IEEE, 2021.
[67] 龙霄潇,程新景,朱 昊,等. 三维视觉前沿进展[J]. 中国图象图形学报,2021,26(6):1389−1428. LONG Xiaoxiao,CHENG Xinjing,ZHU Hao,et al. Recent progress in 3D vision[J]. Journal of Image and Graphics,2021,26(6):1389−1428.
[68] MORAN D, KOSLOWSKY H, KASTEN Y, et al. Deep Permutation Equivariant Structure from Motion[C] //Proceedings of the IEEE International Conference on Computer Vision. Virtual: IEEE, 2021.
[69] SCHONBERGER J L, FRAHM J M. Structure-from-motion revisited[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016.
[70] KLEIN G, MURRAY D. Parallel tracking and mapping for small AR workspaces[C] //Proceedings of the IEEE and ACM International Symposium on Mixed and Augmented Reality. Nara, Japan: IEEE, 2007.
[71] CAMPOS C,ELVIRA R,RODRIGUEZ J J G,et al. Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam[J]. IEEE Transactions on Robotics,2021,37(6):1874−1890. doi: 10.1109/TRO.2021.3075644
[72] EENGEL J, SCHOPS T, CREMERS D. LSD-SLAM: Large-scale direct monocular SLAM[C] //Proceedings of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014.
[73] LI M,MOURIKIS A I. High-precision, consistent EKF-based visual-inertial odometry[J]. The International Journal of Robotics Research,2013,32(6):690−711. doi: 10.1177/0278364913481251
[74] LIN J, ZHANG F. R3LIVE++: a robust, real-time, radiance reconstruction package with a tightly-coupled lidar-inertial-visual state estimator[EB/OL]. (2023-02-14) https://arxiv.org/abs/2109.07982.
[75] 李猛钢,胡而已,朱 华. 煤矿移动机器人LiDAR/IMU紧耦合SLAM方法[J]. 工矿自动化,2022,48(12):68−78. doi: 10.13272/j.issn.1671-251x.2022100061 LI Menggang,HU Eryi,ZHU Hua. LiDAR/IMU tightly-coupled SLAM method for coal mine robot[J]. Journal of Mine Automation,2022,48(12):68−78. doi: 10.13272/j.issn.1671-251x.2022100061
[76] YU C, LIU Z, LIU X J, et al. DS-SLAM: A semantic visual SLAM towards dynamic environments[C] //Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid, Spain: IEEE, 2018.
[77] VON STUMBERG L,CREMERS D. DM-VIO: Delayed marginalization visual-inertial odometry[J]. IEEE Robotics and Automation Letters,2022,7(2):1408−1415. doi: 10.1109/LRA.2021.3140129
[78] PATWARDHAN A,MURAI R,DAVISON A J. Distributing collaborative multi-robot planning with gaussian belief propagation[J]. IEEE Robotics and Automation Letters,2022,8:552−559.
[79] TU C, TAKEULAI E, MIYAJIMA C, et al. Continuous point cloud data compression using SLAM based prediction[C] //Proceedings of the IEEE Intelligent Vehicles Symposium. Los Angeles, USA: IEEE, 2017.
[80] KRUL S,PANTOS C,FRANGULEA M,et al. Visual SLAM for indoor livestock and farming using a small drone with a monocular camera: A feasibility study[J]. Drones,2021,5(2):41. doi: 10.3390/drones5020041
[81] ZHANG S,ZHENG L,TAO W. Survey and evaluation of RGB-D SLAM[J]. IEEE Access,2021,9:21367−21387. doi: 10.1109/ACCESS.2021.3053188
[82] ROSINOL A, LEONARD J J, CARLONE L. NeRF-SLAM: real-time dense monocular slam with neural radiance fields[EB/OL]. (2023-02-14) https://arxiv.org/abs/2210.13641.
[83] XUE G,WEI J,LI R,et al. LeGO-LOAM-SC: an improved simultaneous localization and mapping method fusing LeGO-LOAM and scan context for underground coal mine[J]. Sensors,2022,22(2):520−525. doi: 10.3390/s22020520
[84] PAN W,ZHENG J,WANG L,et al. A Future Perspective on In-Sensor Computing[J]. Engineering,2022,14(7):19−21.
[85] HINTON G,VINYALS O,DEAN J. Distilling the knowledge in a neural network[J]. Computer Science,2015,14(7):38−39.
[86] YIM J, JOO D, BAE J, et al. A gift from knowledge distillation: Fast optimization, network minimization and transfer learning[C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017.
[87] NAGEL M, BAALEN M, BLANKEVOORT T, et al. Data-free quantization through weight equalization and bias correction[C]//Proceedings of the IEEE International Conference on Computer Vision. Seoul, Korea: IEEE, 2019.
[88] BROGI A,FORTI S,GUERRERO C,et al. How to place your apps in the fog: state of the art and open challenges[J]. Software:Practice and Experience,2019,1(1):1−8.
[89] ZHANG J, LI W, OGUNBONA P. Joint geometrical and statistical alignment for visual domain adaptation[C] //Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, USA: IEEE, 2017.
[90] 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.
[91] CHENG J, YE L, GUO Y, et al. Ground crack recognition based on fully convolutional network with multi-scale input[J]. IEEE Access, 2020, 8(1): 53034−53048.
[92] 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. doi: 10.13199/j.cnki.cst.2022-0359 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. doi: 10.13199/j.cnki.cst.2022-0359
[93] 原志明. 上湾煤矿安全智能视频系统设计及应用[J]. 煤炭科学技术, 2017, 45(2): 114-117. YUAN Zhiming. Application and design of safe intelligent video system in Shangwan Mine[J]. 2017, 45(2): 114-117.
[94] 程 健,王瑞彬,郁华森,等. 矿井巷道复杂场景灭点检测方法[J]. 工矿自动化,2021,47(6):25−31. doi: 10.13272/j.issn.1671-251x.2021040097 CHENG Jian,WANG Ruibin,YU Huasen,et al. Vanishing point de-tection method in complex environment of mine roadway[J]. Industry and Mine Automation,2021,47(6):25−31. doi: 10.13272/j.issn.1671-251x.2021040097
[95] 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. doi: 10.13225/j.cnki.jccs.2018.2041 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. doi: 10.13225/j.cnki.jccs.2018.2041
[96] 彭玉敬,刘传安,郜 彤,等. 煤矿企业安全风险防控与预警系统研究[J]. 煤炭技术,2019,38(3):184−187. doi: 10.13301/j.cnki.ct.2019.03.062 PENG Yujing,LIU Chuanan,GAO Tong,et al. Study on safety risk prevention and early warning system of coal mine enterprise[J]. Coal Technology,2019,38(3):184−187. doi: 10.13301/j.cnki.ct.2019.03.062
[97] 徐江陵. 基于物联网的智能化煤矿安全监控系统研究[J]. 煤炭技术,2018,37(8):229−231. doi: 10.13301/j.cnki.ct.2018.08.087 XU Jiangling. Research on intelligent coal mine safety monitoring system based on internet of things[J]. Coal Technology,2018,37(8):229−231. doi: 10.13301/j.cnki.ct.2018.08.087
[98] 王道元,王 俊,孟志斌,等. 煤矿安全风险智能分级管控与信息预警系统[J]. 煤炭科学技术,2021,49(10):136−144. doi: 10.13199/j.cnki.cst.2021.10.019 WANG Daoyuan,WANG Jun,MENG Zhibin,et al. Intelligent hierarchical management & control and information pre-warning system of coal mine safety risk[J]. Coal Science and Technology,2021,49(10):136−144. doi: 10.13199/j.cnki.cst.2021.10.019
[99] 程 健. 煤矿巷道机器人的管线视觉辅助定位与导航方法研究[J]. 煤炭科学技术,2020,48(7):226−232. CHENG Jian. Study on pipeline vision-aided positioning and navigation method for coal mine tunnel robot[J]. Coal Science and Technology,2020,48(7):226−232.
[100] TRIGGS B, MCLAUCHAN P E, HARTLEY R I, et al. Bundle adjustment-a modern synthesis. vision algorithms: theory and practice[J]. Springer Berlin Heidelberg, 2000: 298−372.
[101] 张旭辉. 数字孪生驱动的悬臂式掘进机虚拟操控技术[J]. 计算机集成制造系统, 2021, 27(6): 1617−1627. ZHANG Xuhui. Digital twin-driven virtual control technology of cantilever roadheader[J]. 2021, 27(6): 1617−1627.
[102] 毛善君,鲁守明,李存禄,等. 基于精确大地坐标的煤矿透明化智能综采工作面自适应割煤关键技术研究及系统应用[J]. 煤炭学报,2022,47(1):515−526. MAO Shanjun,LU Shouming,LI Cunlu,et al. Key technologies and system of adaptive coal cutting in transparent intelligent fully mechanized coal mining face based on precise geodetic coordinates[J]. Journal of China Coal Society,2022,47(1):515−526.
[103] 拓宏平,高锦磊,杨一特. 综采工作面视频拼接关键技术[J]. 智能矿山,2022,11:67−69. TUO Hongping,GAO Jinlei,YANG Yite. Key technology of video splicing in fully mechanized mining face[J]. Journal of Intelligent Mine,2022,11:67−69.
[104] 徐华龙. 基于三维视频融合的矿井实时监测技术[J]. 煤矿安全,2021,52(1):136−139. doi: 10.13347/j.cnki.mkaq.2021.01.025 XU Hualong. Mine real-time monitoring technology based on 3D video fusion[J]. Safety in Coal Mines,2021,52(1):136−139. doi: 10.13347/j.cnki.mkaq.2021.01.025
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