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摘要: 聚焦于礦石勘探和將礦石破碎篩分后的皮帶運輸兩個環節，系統總結了深度學習技術在礦石圖像處理中的主要應用，包括礦石分類、粒度分析和異物識別等任務，并分門別類地梳理了完成以上三大任務的常用算法及其優缺點。其中，礦石分類在地質勘探中起著重要作用；粒度分析能為破碎機和傳送皮帶的控制提供參考依據，還能識別出給礦皮帶上過大尺寸的礦石，防止處于給礦皮帶和受礦皮帶之間的轉運緩沖倉內發生堵料事故；異物識別能將皮帶上混在礦石中的有害物品檢測出來。Abstract: Ore is an essential industrial raw material and strategic resource that plays an important role in China’s economic construction. The smart mine aims to build an unmanned, efficient, intelligent, and remote factory to improve quality, reduce cost, save energy, and increase the efficiency of mineral resource extraction. Ore image processing technology can automatically and efficiently complete a series of difficult and repetitive tasks, which constitutes an important part of smart mine construction. However, open-air operation modes, high-dust environments, and ore diversity have brought great challenges to ore image processing. Benefiting from its strong automatic feature extraction ability, deep learning can deeply perceive a complex environment, which enables it to play an important role in the ore image processing field and help traditional mining companies transform into efficient, green, and intelligent enterprises. This paper focuses on two production stages, including ore prospecting and belt transportation. We systematically summarize the main applications of deep learning in ore image processing, including ore classification, particle size analysis, and foreign material recognition, sort out the corresponding algorithms, and analyze their advantages and disadvantages. Specifically, according to the number of ores in an image, ore classification is divided into single-object and multi-object classifications. Single-object classification is mostly addressed by image classification networks, while multi-object classification is mostly accomplished by object detection and semantic segmentation networks. Single-object classification plays an important role in geological prospecting. Particle size refers to the size information of ores in an image. Generally, it can be divided into three modes: particle size statistics, particle size classification, and large block detection. Among these modes, the first and the third are mainly used in actual industrial production. Particle size statistics are determined mostly using semantic segmentation networks and can provide a reference for the control of crushers and conveyor belts. Large block detection is performed mostly by adopting object detection networks and can identify the oversized ore on an ore feeding belt and prevent material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Foreign material recognition detects harmful objects mixed in the ores on the belt to ensure product quality and prevent the belt from tearing. Object detection technology is often used to complete the task of foreign material recognition.
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圖 1 礦石生產流程與礦石圖像處理任務. (a) 生產流程; (b) 礦石圖像處理任務分類
Figure 1. Ore production process and the ore image processing task: (a) production process; (b) task classification
圖 2 深度學習技術分類. (a) 圖像分類; (b) 目標檢測; (c) 語義分割
Figure 2. Deep learning technology classification: (a) image classification; (b) object detection; (c) semantic segmentation
圖 3 不同類型鐵礦石示例. (a) 赤鐵礦; (b) 假象礦; (c) 褐鐵礦; (d) 透閃礦
Figure 3. Examples of different types of iron ore: (a) hematite; (b) false mineral; (c) limonite; (d) tremolite
圖 4 多個體礦石分類技術. (a) 目標檢測; (b) 語義分割[50]
Figure 4. Multi-object ore classification technology: (a) object detection; (b) semantic segmentation
圖 9 基于不同損失函數的U-Net礦石圖像分割結果. (a) 礦石原圖; (b) 二值交叉熵損失函數; (c) 焦點損失函數
Figure 9. U-Net ore image segmentation result based on different losses: (a) original ore image; (b) BCE; (c) focal loss
圖 10 基于Faster R-CNN的皮帶大塊礦石檢測. (a) 原圖; (b) 標簽; (c) 檢測結果
Figure 10. Large block ore detection based on Faster R-CNN: (a) original image; (b) label; (c) detection result
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參考文獻
[1] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278 doi: 10.1109/5.726791 [2] Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115(3): 211 doi: 10.1007/s11263-015-0816-y [3] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84 doi: 10.1145/3065386 [4] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition // Proceedings of the International Conference on Learning Representations. San Diego, 2015. [5] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, 2015: 1 [6] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 770 [7] Hu J, Shen L, Sun G. Squeeze-and-Excitation networks // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 7132 [8] Howard A G, Zhu M, Chen B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications [J/OL]. arXiv Online (2017-4-17) [2022-01-23].https://arxiv.org/abs/1704.04861 [9] Zhang X Y, Zhou X Y, Lin M X, et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 6848 [10] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need // Proceedings of the 31st International Conference on Advances in Neural Information Processing Systems. Long Beach, 2017: 6000 [11] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: Transformers for image recognition at scale // Proceedings of the International Conference on Learning Representations. Addis Ababa, 2021 [12] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Tntell, 2017, 39(6): 1137 doi: 10.1109/TPAMI.2016.2577031 [13] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 779 [14] Redmon J, Farhadi A. YOLO9000: Better, faster, stronger // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, 2017: 7263 [15] Ge Z, Liu S T, Wang F, et al. YOLOX: Exceeding YOLO series in 2021 [J/OL]. arXiv Online (2021-7-18) [2022-01-23].https://arxiv.org/abs/2107.08430 [16] Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector // Proceedings of the European Conference on Computer Vision. Amsterdam, 2016: 21 [17] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points [J/OL]. arXiv Online (2019-4-16) [2022-01-23].https://arxiv.org/abs/1904.07850 [18] Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers // Proceedings of the European Conference on Computer Vision. Glasgow, 2020: 213 [19] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, 2015: 3431 [20] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation // Proceedings of the International Conference on Medical Image Computing & Computer-assisted Intervention. Munich, 2015: 234 [21] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell, 2018, 40(4): 834 doi: 10.1109/TPAMI.2017.2699184 [22] Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, 2017: 6230 [23] Milletari F, Navab N, Ahmadi S A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation // Proceedings of the Fourth International Conference on 3D Vision. Palo Alto, 2016: 565 [24] Huang Z L, Wang X G, Huang L C, et al. CCNet: Criss-cross attention for semantic segmentation // Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, 2019: 603 [25] Zheng S X, Lu J C, Zhao H S, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Nashville, 2021: 6877 [26] Su L L, Cao X G, Ma H W, et al. Research on coal gangue identification by using convolutional neural network // Proceedings of the IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. Xi’an, 2018: 810 [27] Pu Y Y, Apel D B, Szmigiel A, et al. Image recognition of coal and coal gangue using a convolutional neural network and transfer learning. Energies, 2019, 12(9): 1 [28] Wang L G, Chen S J, Jia M T, et al. Beneficiation method of wolframite image recognition based on deep learning. Chin J Nonferr Met, 2020, 30(5): 1192 doi: 10.11817/j.ysxb.1004.0609.2020-35801王李管, 陳斯佳, 賈明滔, 等. 基于深度學習的黑鎢礦圖像識別選礦方法. 中國有色金屬學報, 2020, 30(5):1192 doi: 10.11817/j.ysxb.1004.0609.2020-35801 [29] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 2818 [30] Zhang Y, Li M C, Han S. Automatic identification and classification in lithology based on deep learning in rock images. Acta Petrol Sin, 2018, 34(2): 333張野, 李明超, 韓帥. 基于巖石圖像深度學習的巖性自動識別與分類方法. 巖石學報, 2018, 34(2):333 [31] Baraboshkin E E, Ismailova L S, Orlov D M, et al. Deep convolutions for in-depth automated rock typing. Comput Geosci, 2020, 135: 104330 doi: 10.1016/j.cageo.2019.104330 [32] Bai L, Yao Y, Li S T, et al. Mineral composition analysis of rock image based on deep learning feature extraction. China Min Mag, 2018, 27(7): 178 doi: 10.12075/j.issn.1004-4051.2018.07.038白林, 姚鈺, 李雙濤, 等. 基于深度學習特征提取的巖石圖像礦物成分分析. 中國礦業, 2018, 27(7):178 doi: 10.12075/j.issn.1004-4051.2018.07.038 [33] Lloyd S. Least Squares quantization in PCM. IEEE Trans Inf Theory, 1982, 28(2): 129 doi: 10.1109/TIT.1982.1056489 [34] Li M C, Liu C Z, Zhang Y, et al. A deep learning and intelligent recognition method of image data for rock mineral and its implementation. Geotectonica Metallog, 2020, 44(2): 203 doi: 10.16539/j.ddgzyckx.2020.02.004李明超, 劉承照, 張野, 等. 耦合顏色和紋理特征的礦物圖像數據深度學習模型與智能識別方法. 大地構造與成礦學, 2020, 44(2):203 doi: 10.16539/j.ddgzyckx.2020.02.004 [35] Liu C Z, Li M C, Zhang Y, et al. An enhanced rock mineral recognition method integrating a deep learning model and clustering algorithm. Minerals, 2019, 9(9): 516 doi: 10.3390/min9090516 [36] Zeng X, Xiao Y C, Ji X H, et al. Mineral identification based on deep learning that combines image and Mohs hardness. Minerals, 2021, 11(5): 506 doi: 10.3390/min11050506 [37] Tan M, Le Q. EfficientNet: Rethinking model scaling for convolutional neural networks // Proceedings of the International Conference on Machine Learning. Long Beach, 2019: 6105 [38] Yun S, Han D, Chun S, et al. CutMix: Regularization strategy to train strong classifiers with localizable features // Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, 2019: 6022 [39] Liang Y, Cui Q, Luo X, et al. Research on classification of fine-grained rock images based on deep learning. Comput Intell Neurosci, 2021, 2021: 5779740 [40] Iglesias J C A, Santos R B M, Paciornik S. Deep learning discrimination of quartz and resin in optical microscopy images of minerals. Miner Eng, 2019, 138: 79 doi: 10.1016/j.mineng.2019.04.032 [41] Guo Y J, Zhou Z, Lin H X, et al. The mineral intelligence identification method based on deep learning algorithms. Earth Sci Front, 2020, 27(5): 39 doi: 10.13745/j.esf.sf.2020.5.45郭艷軍, 周哲, 林賀洵, 等. 基于深度學習的智能礦物識別方法研究. 地學前緣, 2020, 27(5):39 doi: 10.13745/j.esf.sf.2020.5.45 [42] Ran X J, Xue L F, Zhang Y Y, et al. Rock classification from field image patches analyzed using a deep convolutional neural network. Mathematics, 2019, 7(8): 755 doi: 10.3390/math7080755 [43] Lima R P D, Bonar A, Coronado D D, et al. Deep convolutional neural networks as a geological image classification tool. Sediment Rec, 2019, 17(2): 4 doi: 10.2110/sedred.2019.2.4 [44] Xiao D, Le B T, Ha T T L. Iron ore identification method using reflectance spectrometer and a deep neural network framework. Spectrochimica Acta A Mol Biomol Spectrosc, 2021, 248(10): 119168 [45] Han S, Li H, Li M C, et al. Measuring rock surface strength based on spectrograms with deep convolutional networks. Comput Geosci, 2019, 133: 104312 doi: 10.1016/j.cageo.2019.104312 [46] Liu X B, Wang H Y, Jing H D, et al. Research on intelligent identification of rock types based on Faster R-CNN method. IEEE Access, 2020, 8: 21804 doi: 10.1109/ACCESS.2020.2968515 [47] Xu Z H, Ma W, Lin P, et al. Deep learning of rock images for intelligent lithology identification. Comput Geosci, 2021, 154: 104799 doi: 10.1016/j.cageo.2021.104799 [48] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection [J/OL]. arXiv Online (2020-4-23) [2022-01-23].https://arxiv.org/abs/2004.10934 [49] Deng T, Yu Y. Research on ore identification and separation based on improved PSO-Faster R-CNN algorithm. Min Res Dev, 2021, 41(2): 178鄧田, 余翼. 基于PSO-Faster R-CNN改進算法的礦石識別分類研究. 礦業研究與開發, 2021, 41(2):178 [50] Yang H, Huang C, Wang L, et al. An improved encoder-decoder network for ore image segmentation. IEEE Sens J, 2021, 21(10): 11469 doi: 10.1109/JSEN.2020.3016458 [51] Luo X Y, Liu S, Tang W C, et al. Research on identification and location of blocked ore at ore bin inlet based on Mask R-CNN. Nonferrous Met Sci Eng, 2022, 13(1): 101羅小燕, 劉順, 湯文聰, 等. 基于Mask R-CNN的礦倉入料口堵塞礦石識別定位研究. 有色金屬科學與工程, 2022, 13(1):101 [52] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN // Proceedings of the IEEE International Conference on Computer Vision. Venice, 2017: 2980 [53] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern, 1979, 9(1): 62 doi: 10.1109/TSMC.1979.4310076 [54] Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell, 1986(6): 679 [55] Zhang W, Jiang D L. The marker-based watershed segmentation algorithm of ore image // Proceedings of the IEEE International Conference on Communication Software and Networks. Xi’an, 2011: 472 [56] Xiao D, Liu X W, Le B T, et al. An ore image segmentation method based on RDU-Net model. Sensors, 2020, 20(17): 4979 doi: 10.3390/s20174979 [57] Wang W, Li Q, Xiao C Y, et al. An improved boundary-aware U-Net for ore image semantic segmentation. Sensors, 2021, 21(8): 2615 doi: 10.3390/s21082615 [58] Xie S N, Tu Z W. Holistically-nested edge detection // Proceeding of the IEEE International Conference on Computer Vision. Santiago, 2015: 1395 [59] Yuan L, Duan Y Y. A method of ore image segmentation based on deep learning // Proceedings of the Intelligent Computing Methodologies. Wu’han, 2018: 508 [60] Gu Q H, Wei F W, Guo M L, et al. Segmentation method of broken ore image based on improved HED network model. Laser Optoelectron Prog, 2022, 59(2): 262顧清華, 危發文, 郭夢利, 等. 基于改進HED網絡模型的破碎礦石圖像分割方法. 激光與光電子學進展, 2022, 59(2):262 [61] Xu J C, Jin G Q, Zhu T Y, et al. Segmentation of rock images based on U-Net. Ind Control Comput, 2018, 31(4): 98 doi: 10.3969/j.issn.1001-182X.2018.04.040徐江川, 金國強, 朱天奕, 等. 基于深度學習U-Net模型的石塊圖像分割算法. 工業控制計算機, 2018, 31(4):98 doi: 10.3969/j.issn.1001-182X.2018.04.040 [62] Ye F Q, Jiang Z H, Zhou H, et al. Blast furnace material image segmentation based on multi-layer feature fusion U-Net // Proceedings of the 2020 China Automation Congress. Shanghai, 2020: 1葉飛強, 蔣朝輝, 周昊, 等. 基于多層特征融合U-Net高爐爐料礦石圖像分割 // 2020中國自動化大會. 上海, 2020: 1 [63] Song W, Zheng N, Liu X C, et al. An improved U-Net convolutional networks for seabed mineral image segmentation. IEEE Access, 2019, 7: 82744 doi: 10.1109/ACCESS.2019.2923753 [64] Dai J F, Qi H Z, Xiong Y W, et al. Deformable convolutional networks // Proceedings of the IEEE International Conference on Computer Vision. Venice, 2017: 764 [65] Liu X B, Zhang Y W. Ore image segmentation method of conveyor belt based on U-Net and Res_UNet models. J Northeast Univ, 2019, 40(11): 1623柳小波, 張育維. 基于U-Net和Res_UNet模型的傳送帶礦石圖像分割方法. 東北大學學報, 2019, 40(11):1623 [66] Liu X B, Zhang Y W, Jing H D, et al. Ore image segmentation method using U-Net and Res_UNet convolutional networks. RSC Adv, 2020, 10(16): 9396 doi: 10.1039/C9RA05877J [67] Chen H, Qi X J, Yu L Q, et al. DCAN: deep contour-aware networks for accurate gland segmentation // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 2487 [68] Shen H C, Wang R X, Zhang J G, et al. Boundary-aware fully convolutional network for brain tumor segmentation // Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, 2017: 433 [69] Oda H, Roth H R, Chiba K, et al. BESNet: Boundary-enhanced segmentation of cells in histopathological images // Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada, 2018: 228 [70] Li X T, Li X, Zhang L, et al. Improving semantic segmentation via decoupled body and edge supervision // Proceedings of the European Conference on Computer Vision. Glasgow, 2020: 435 [71] Cheng T H, Wang X G, Huang L C, et al. Boundary-preserving Mask R-CNN // Proceedings of the European Conference on Computer Vision. Glasgow, 2020: 660 [72] Yu C Q, Wang J B, Peng C, et al. Learning a discriminative feature network for semantic segmentation // Proceedings of the Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 1857 [73] Xue Z F, Chen L, Liu Z T, et al. Rock segmentation visual system for assisting driving in TBM construction. Mach Vis Appl, 2021, 32: 77 doi: 10.1007/s00138-021-01203-8 [74] Li H, Pan C W, Chen Z Y, et al. Ore image segmentation method based on U-Net and watershed. Comput Mater Continua, 2020, 65(1): 563 doi: 10.32604/cmc.2020.09806 [75] Suprunenko V V. Ore particles segmentation using deep learning methods. J Phys:Conf Ser, 2020, 1679(4): 042089 doi: 10.1088/1742-6596/1679/4/042089 [76] Wang Z, Zheng X, Li D Y, et al. A VGGNet-like approach for classifying and segmenting coal dust particles with overlapping regions. Comput Ind, 2021, 132: 103506 doi: 10.1016/j.compind.2021.103506 [77] Duan J X, Liu X Y, Wu X, et al. Detection and segmentation of iron ore green pellets in images using lightweight U-Net deep learning network. Neural Comput Applic, 2020, 32(10): 5775 doi: 10.1007/s00521-019-04045-8 [78] Duan J X, Liu X Y. Online monitoring of green pellet size distribution in haze-degraded images based on VGG16-LU-Net and haze judgment. IEEE Trans Instrum Meas, 2021, 70: 1 [79] Hu J, Wang W, Liu T H, et al. An improved segmentation algorithm of broken ore image. Internet Things Technol, 2021, 11(11): 89 doi: 10.16667/j.issn.2095-1302.2021.11.026胡健, 王偉, 劉太和, 等. 一種改進的破碎口礦石圖像分割算法. 物聯網技術, 2021, 11(11):89 doi: 10.16667/j.issn.2095-1302.2021.11.026 [80] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 318 doi: 10.1109/TPAMI.2018.2858826 [81] Liu Y, Zhang Z L, Liu X, et al. Efficient image segmentation based on deep learning for mineral image classification. Adv Powder Technol, 2021, 32(10): 3885 doi: 10.1016/j.apt.2021.08.038 [82] Zhang Z L. Particle overlapping error correction for coal size distribution estimation by image analysis. Int J Miner Process, 2016, 155: 136 doi: 10.1016/j.minpro.2016.08.016 [83] Zhang Z L, Hu Q, Zhang Z W, et al. Effect and correction of segregation error in coal size distribution estimation by image analysis on a conveyor belt. J Phys:Conf Ser, 2019, 1324(1): 012032 doi: 10.1088/1742-6596/1324/1/012032 [84] Olivier L E, Maritz M G, Craig I K. Estimating ore particle size distribution using a deep convolutional neural network. IFAC-PapersOnLine, 2020, 53(2): 12038 doi: 10.1016/j.ifacol.2020.12.740 [85] Olivier L E, Maritz M G, Craig I K. Deep convolutional neural network for mill feed size characterization. IFAC-PapersOnLine, 2019, 52(14): 105 doi: 10.1016/j.ifacol.2019.09.172 [86] Du J Y, Hao L, Wang Y Y, et al. A detection method for large blocks in underground coal transportation. Ind Mine Autom, 2020, 46(5): 63 doi: 10.13272/j.issn.1671-251x.2019090067杜京義, 郝樂, 王悅陽, 等. 一種煤礦井下輸煤大塊物檢測方法. 工礦自動化, 2020, 46(5):63 doi: 10.13272/j.issn.1671-251x.2019090067 [87] Redmon J, Farhadi A. YOLOv3: An incremental improvement [J/OL]. arXiv Online (2018-4-8) [2022-01-23].https://arxiv.org/abs/1804.02767 [88] Bo J W, Zhang C T, Fan C L, et al. Ore conveyor belt sundries detection based on improved YOLOv3. Comput Eng Appl, 2021, 57(21): 248 doi: 10.3778/j.issn.1002-8331.2105-0025薄景文, 張春堂, 樊春玲, 等. 改進YOLOv3的礦石輸送帶雜物檢測方法. 計算機工程與應用, 2021, 57(21):248 doi: 10.3778/j.issn.1002-8331.2105-0025 [89] Sandler M, Howard A, Zhu M L, et al. MobileNetV2: Inverted residuals and linear bottlenecks // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 4510 [90] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module // Proceedings of the European Conference on Computer Vision. Munich, 2018: 3 [91] Hu J H, Gao Y, Zhang H J, et al. Research on the identification method of non-coal foreign object of belt conveyor based on deep learning. Ind Mine Autom, 2021, 47(6): 57 doi: 10.13272/j.issn.1671-251x.2021020041胡璟皓, 高妍, 張紅娟, 等. 基于深度學習的帶式輸送機非煤異物識別方法. 工礦自動化, 2021, 47(6):57 doi: 10.13272/j.issn.1671-251x.2021020041 [92] Ren Z L, Zhu Y C. Research on foreign objects recognition of coal mine belt transportation with improved CenterNet algorithm. Control Eng China,https://doi.org/10.14107/j.cnki.kzgc.20200792任志玲, 朱彥存. 改進CenterNet算法的煤礦皮帶運輸異物識別研究. 控制工程,https://doi.org/10.14107/j.cnki.kzgc.20200792 [93] Huang Z J, Li F M, Luan X D, et al. A weakly supervised method for mud detection in ores based on deep active learning. Math Probl Eng, 2020, 2020: 3510313 [94] Zhao X J, Li J. A method of coal gangue detection based on deep learning. J Min Sci Technol, 2021, 6(6): 730 doi: 10.19606/j.cnki.jmst.2021.06.012趙學軍, 李建. 一種基于深度學習的煤矸石檢測方法. 礦業科學學報, 2021, 6(6):730 doi: 10.19606/j.cnki.jmst.2021.06.012 [95] Liu Y, Zhang Z L, Liu X, et al. Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size. Miner Eng, 2021, 172: 107020 doi: 10.1016/j.mineng.2021.107020 [96] Liu Y, Zhang Z L, Liu X, et al. Deep learning-based image classification for online multi-coal and multi-class sorting. Comput Geosci, 2021, 157: 104922 doi: 10.1016/j.cageo.2021.104922 [97] Liu Y, Zhang Z L, Liu X, et al. Deep learning based mineral image classification combined with visual attention mechanism. IEEE Access, 2021, 9: 98091 doi: 10.1109/ACCESS.2021.3095368 [98] Fu Y, Aldrich C. Quantitative ore texture analysis with convolutional neural networks. IFAC-PapersOnLine, 2019, 52(14): 99 doi: 10.1016/j.ifacol.2019.09.171 -
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