Chinese Journal of Lasers, Volume. 48, Issue 16, 1611001(2021)
Fast Location of Coal Gangue Based on Multispectral Band Selection
[2] Li J Y, Wang J M. Comprehensive utilization and environmental risks of coal gangue: a review[J]. Journal of Cleaner Production, 239, 117946(2019).
[3] Gui X H, Liu J T, Cao Y J et al. Coal preparation technology: status and development in China[J]. Energy & Environment, 26, 997-1013(2015).
[4] Chelgani S C, Parian M, Parapari P S et al. A comparative study on the effects of dry and wet grinding on mineral flotation separation-a review[J]. Journal of Materials Research and Technology, 8, 5004-5011(2019).
[5] Wang G F, Wang H, Ren H W et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society, 43, 295-305(2018).
[6] Shang D Y, Wang Y W, Yang Z Y et al. Study on comprehensive calibration and image sieving for coal-gangue separation parallel robot[J]. Applied Sciences, 10, 7059(2020).
[7] Zheng K H, Du C L, Li J P et al. Underground pneumatic separation of coal and gangue with large size (≥ 50 mm) in green mining based on the machine vision system[J]. Powder Technology, 278, 223-233(2015).
[8] Liang X G, Li Y F, Li Y et al. The intelligent dry cleaning technology: study, application and developing trend[J]. Coal Preparation Technology, 92-96, 102(2019).
[9] Guo Y C, Yu Z S, Lu Y C. Research on photoelectric intelligent separation technology of coal and gangue based on NP-FSVM with the PSO algorithm[J]. Coal Science and Technology, 47, 13-19(2019).
[10] Zhao Y D, He X M. Recognition of coal and gangue based on X-ray[J]. Applied Mechanics and Materials, 275/276/277, 2350-2353(2013).
[11] Cao X G, Li Y, Wang P et al. Research status of coal-gangue identification method and its prospect[J]. Industry and Mine Automation, 46, 38-43(2020).
[12] Rao Z Y, Wu J T, Li M. Coal-gangue image classification method[J]. Industry and Mine Automation, 46, 69-73(2020).
[13] Dou D Y, Wu W Z, Yang J G et al. Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM[J]. Powder Technology, 356, 1024-1028(2019).
[14] Lai W H, Zhou M R, Hu F et al. A study of multispectral technology and two-dimension autoencoder for coal and gangue recognition[J]. IEEE Access, 8, 61834-61843(2020).
[15] Andreopoulos A, Tsotsos J K. 50 years of object recognition: directions forward[J]. Computer Vision and Image Understanding, 117, 827-891(2013).
[16] Sun J, Guo D B, Yang T T et al. Real-time object detection based on improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 221505(2020).
[17] Li C Y, Yao J M, Lin Z X et al. Object detection method based on improved YOLO lightweight network[J]. Laser & Optoelectronics Progress, 57, 141003(2020).
[18] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).
[19] Deepa R, Tamilselvan E, Abrar E S et al. Comparison of Yolo, SSD, faster RCNN for real time tennis ball tracking for action decision networks[C]. //2019 International Conference on Advances in Computing and Communication Engineering (ICACCE), April 4-6, 2019, Sathyamangalam, India., 1-4(2019).
[20] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).
[21] Zhang X X, Zhu X. Moving vehicle detection in aerial infrared image sequences via fast image registration and improved YOLOv3 network[J]. International Journal of Remote Sensing, 41, 4312-4335(2020).
[24] Tian Y N, Yang G D, Wang Z et al. Apple detection during different growth stages in orchards using the improved YOLO-V3 model[J]. Computers and Electronics in Agriculture, 157, 417-426(2019).
[25] Li D J, Zhang Z X, Xu Z H et al. An image-based hierarchical deep learning framework for coal and gangue detection[J]. IEEE Access, 7, 184686-184699(2019).
[26] Lü Z, Wang W D, Xu Z Q et al. Cascade network for detection of coal and gangue in the production context[J]. Powder Technology, 377, 361-371(2021).
[27] Lai W H, Zhou M R, Hu F et al. Coal gangue detection based on multi-spectral imaging and improved YOLOv4[J]. Acta Optica Sinica, 40, 2411001(2020).
[28] Guo T, Hua W S, Liu X et al. Rapid hyperspectral band selection approach based on clustering and optimal index algorithm[J]. Optical Technique, 42, 496-500(2016).
[29] Acharya T, Yang I, Lee D. Land cover classification of imagery from landsat operational land imager based on optimum index factor[J]. Sensors & Materials, 30, 1753-1764(2018).
[30] Ren S Q, He K M, 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, 39, 1137-1149(2017).
[31] Neubeck A, van Gool L. Efficient non-maximum suppression[C]. //18th International Conference on Pattern Recognition (ICPR’06), August 20-24, 2006, Hong Kong, China., 850-855(2006).
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Wenhao Lai, Mengran Zhou, Jinguo Wang, Tianyu Hu, Xixi Kong, Feng Hu, Kai Bian, Ziwei Zhu. Fast Location of Coal Gangue Based on Multispectral Band Selection[J]. Chinese Journal of Lasers, 2021, 48(16): 1611001
Category: spectroscopy
Received: Dec. 17, 2020
Accepted: Feb. 18, 2021
Published Online: Aug. 6, 2021
The Author Email: Mengran Zhou (mrzhou8521@163.com)