Citation: | Zhao Xuejun, Li Jian. A method of coal gangue detection based on deep learning[J]. Journal of Mining Science and Technology, 2021, 6(6): 730-736. doi: 10.19606/j.cnki.jmst.2021.06.012 |
[1] |
孙继平. 煤矿安全生产监控与通信技术[J]. 煤炭学报, 2010, 35(11): 1925-1929. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201011034.htm
Sun Jiping. Technologies of monitoring and communication in the coal mine[J]. Journal of China Coal Society, 2010, 35(11): 1925-1929. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201011034.htm
|
[2] |
宋曦, 丁文梅, 宁云才, 等. 煤矿安全生产管理体系优化研究: 以陕西某煤矿为例[J]. 矿业科学学报, 2019, 4(2): 187-194. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201902012.htm
Song Xi, Ding Wenmei, Ning Yuncai, et al. Research on the optimization of coal mine safety production management system—take a coal mine in Shaanxi as an example[J]. Journal of Mining Science and Technology, 2019, 4(2): 187-194. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201902012.htm
|
[3] |
杨捷, 武继龙, 晋俊宇. 矸石、粉煤灰高浓度料浆矸石颗粒悬浮性研究[J]. 矿业科学学报, 2019, 4(2): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201902005.htm
Yang Jie, Wu Jilong, Jin Junyu. Study on the suspended properties of gangue particles with high concentration of gangue and fly ash[J]. Journal of Mining Science and Technology, 2019, 4(2): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201902005.htm
|
[4] |
王卫东, 张楠, 靳立章. 超声波同步处理强化煤泥浮选的试验研究[J]. 矿业科学学报, 2019, 4(4): 357-364. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201904010.htm
Wang Weidong, Zhang Nan, Jin Lizhang. Experiment study on fine coal slime flotation with simultaneous ultrasonic treatment[J]. Journal of Mining Science and Technology, 2019, 4(4): 357-364. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX201904010.htm
|
[5] |
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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201801016.htm
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 and Technology, 2018, 49(1): 118-123. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201801016.htm
|
[6] |
Huang S R, Bergström N, Yamakawa Y, et al. Applying high-speed vision sensing to an industrial robot for high-performance position regulation under uncertainties[J]. Sensors, 2016, 16(8): 1195. doi: 10.3390/s16081195
|
[7] |
Nandi C S, Tudu B P, Koley C. A machine vision-based maturity prediction system for sorting of harvested mangoes[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(7): 1722-1730. doi: 10.1109/TIM.2014.2299527
|
[8] |
Bao S Q, Chung A C S. Multi-scale structured CNN with label consistency for brain MR image segmentation[J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018, 6(1): 113-117.
|
[9] |
Uijlings J R R, Sande K, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171. doi: 10.1007/s11263-013-0620-5
|
[10] |
刘富强, 钱建生, 王新红, 等. 基于图像处理与识别技术的煤矿矸石自动分选[J]. 煤炭学报, 2000, 25(5): 534-537. doi: 10.3321/j.issn:0253-9993.2000.05.020
Liu Fuqiang, Qian Jiansheng, Wang Xinhong, et al. Automatic separation of waste rock in coal mine based on image procession and recognition[J]. Journal of China Coal Society, 2000, 25(5): 534-537. doi: 10.3321/j.issn:0253-9993.2000.05.020
|
[11] |
于国防, 邹士威, 秦聪. 图像灰度信息在煤矸石自动分选中的应用研究[J]. 工矿自动化, 2012, 38(2): 36-39. https://www.cnki.com.cn/Article/CJFDTOTAL-MKZD201202013.htm
Yu Guofang, Zou Shiwei, Qin Cong. Application research of image gray information in automatic separation of coal and gangue[J]. Industry and Mine Automation, 2012, 38(2): 36-39. https://www.cnki.com.cn/Article/CJFDTOTAL-MKZD201202013.htm
|
[12] |
Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. doi: 10.1109/TSMC.1979.4310076
|
[13] |
Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698. http://www.researchgate.net/publication/301840781_A_computational_approach_to_edge_detection_IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence
|
[14] |
Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. [2020-08-10]https://arxiv.org/abs/1804.02767,2018.
|
[15] |
He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA: IEEE, 2016: 770-778.
|
[16] |
Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size[C]//International Conference on Learning Representations(ICLR), 2017.
|
[17] |
Howard A G, Zhu M L, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. [2020-08-19]https://arxiv.org/abs 1704.04861, 2017.
|
[18] |
He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. doi: 10.1109/TPAMI.2015.2389824
|
[19] |
Liu S T, Huang D, Wang Y H. Learning spatial fusion for single-shot object detection[EB/OL]. [2020-08-19]https://arxiv.org/abs1911.09516,2019.
|
[20] |
Cipolla R, Gal Y, Kendall A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA: IEEE, 2018: 7482-7491.
|
[21] |
Pang J M, Chen K, Shi J P, et al. Libra R-CNN: towards balanced learning for object detection[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019: 821-830.
|
[22] |
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA. IEEE, 2014: 580-587.
|
[23] |
Yu J H, Jiang Y N, Wang Z Y, et al. UnitBox: an advanced object detection network[C]// Proceedings of the 24th ACM international conference on Multimedia. Amsterdam The Netherlands. New York, NY, USA: ACM, 2016.
|
[24] |
Rezatofighi H, Tsoi N, Gwak J, 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 (CVPR). Long Beach, CA, USA: IEEE, 2019: 658-666.
|
[25] |
Zheng Z H, Wang P, Liu W, et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12993-13000. doi: 10.1609/aaai.v34i07.6999
|