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Fast identification model for coal and gangue based on the improved tiny YOLO v3

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Abstract

In the process of coal mining, the coal quality is greatly reduced due to the mixing of gangue, and the stacking and burning of coal and gangue can cause serious environmental pollution. The fast identification of coal and gangue is an important technology for gangue sorting. Considering tiny YOLO v3 has the advantages of fast running speed, simple network and good effectiveness, an improved tiny-YOLO-v3-based fast identification model is proposed in this paper, which including the spatial pyramid pooling (SPP) net, the squeeze-and-excitation (SE) module and the dilated convolution. Firstly, the SPP net preprocesses the input images into acceptable size via a single convolution layer. Then, the SE module, which can strengthen the attention among channels in RGB image, is adopted to capture the key information and enhance the sensitivity of network accurately. Finally, the dilated convolution, which can enlarge the receptive filed without increasing parameters, is used to further optimize and realize the fast identification of coal and gangue. The experiments show that, compared with the tiny YOLO v3 the average intersection over union (Avg IOU) of proposed model is as high as 0.39%, the required time for each image and the loss reduced by 7.41 and 53.01%, respectively. The mean Average Precision (mAP@0.5) of the proposed network is 3.12% higher than Faster RCNN, reaching 0.964. It is an efficient fast identification model for gangue-sorting.

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References

  1. Deng, J.: Study on the characteristics and comprehensive utilization of coal gangue. Coal. Technol. 028(6), 149–150 (2009)

    Google Scholar 

  2. Zhang, X.: Design of real-time monitoring system for coal mine operation environment based on lora technology. Automa. Instrum. 40(03), 73–77 (2019)

    Google Scholar 

  3. Guo, X.J.: Research and application of coal gangue separation technology. Coal. Eng. 1, 74–76 (2019)

    Google Scholar 

  4. Xu, J.Q.: Technology and management of heavy medium coal preparation. Coal. Technol. 30(08), 137–139 (2011)

    Google Scholar 

  5. Yang, D., Chen, W.F., Fang, Y.Q.: Research and application of mechanical gangue separator system. Coal. Sci. Technol. 29(08), 15–16 (2001)

    Google Scholar 

  6. Wang, Z., Pan, H.G.: Image identification of coal dust particles based on improved differential evolution particle swarm. J China Coal Soc 45(02), 695–702 (2020)

  7. Wang, Z., Xu, Z., Pan, H.G., Li, D.Y.: Information entropy multi-decision attribute reduction fuzzy rough set for dust particulate imagery characteristic extraction. IEEE. Acess. 8, 77865–77874 (2020)

    Article  Google Scholar 

  8. Han, J.W., Zhang, D.W., Cheng, G., Liu, N., Xu, D.: Advanced deep-learning techniques for salient and categoryspecific object detection: a survey. IEEE. Signal. Process. Mag. 35(1), 84–100 (2018)

    Article  Google Scholar 

  9. Chin, T.W., Yu, C.L., Halpern, M., Genc, H., Tsao, S.L., Janapa Reddi, V.J.: Domain-specific approximation for object detection. IEEE. Micro. 38(1), 31–40 (2018)

    Article  Google Scholar 

  10. Mas, J., Panadero, T., Botella, G., Barrio, A.A., Garca, C.: Cnn inference acceleration using low-power devices for human monitoring and security scenarios. Comput. Electr. Eng. 88, 106859 (2020)

    Article  Google Scholar 

  11. Channappa G.D V, Kanagavalli R.: Detecting and tracking of multiple objects in a single frame with yolo. International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp 981–984 (2021)

  12. Han, Y., Chung, S.L., Xiao, Q., Wang, J.S., Su, S.F.: Pharmaceutical blister package identification based on induced deep learning. IEEE. Access. 9, 101344–101356 (2021)

    Article  Google Scholar 

  13. Li, D.J., Zhang, Z.X., Xu, Z.H., Xu, L.L., Meng, G.Y., Li, Z., Chen, S.Y.: An image-based hierarchical deep learning framework for coal and gangue detection. IEEE. Access. 7, 184686–184699 (2019)

    Article  Google Scholar 

  14. Pu, Y.Y., Apel, D., Szmigiel, A., Chen, J.: Image recognition of coal and coal gangue using a convolutional neural network and transfer learning. Energies 12(9), 1735–1746 (2019)

    Article  Google Scholar 

  15. Pan, X.J., Tang, F., Dong, W.M., Gu, Y., Xu, C.S.: Self-supervised feature augmentation for large image object detection. IEEE. Transact. Image. Process 99, 1–1 (2020)

    Google Scholar 

  16. Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Ferrari, V.: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. Int. J. Comput. Vision. 128(7), 1956–1981 (2020)

    Article  Google Scholar 

  17. Li, G.B., Gan, Y.K., Wu, H.J., Xiao, N., Lin, L.: Cross-modal attentional context learning for rgb-d object detection. IEEE Trans. Image Process. 28(4), 1591–1601 (2019)

    Article  MathSciNet  Google Scholar 

  18. Wang, Z., Xu, Z., Li, D.Y., Zhang, H., Yang, Y., Pan, H.G.: A vggnet-like approach for classifying and segmenting coal dust particles with overlapping regions. Comput. Indus. 132, 0166–3615 (2021)

    Article  Google Scholar 

  19. Lei, X.Y., Pan, H.G., Huang, X.D.: A dilated cnn model for image classification. IEEE. Access. 7, 124087–124095 (2019)

    Article  Google Scholar 

  20. Pan, H.G., Fan, W., Huang, X.D., Lei, X.Y., Yang, X.L.: The enhanced deep plug-and-play super-resolution algorithm with residual channel attention networks. J. Intell. Fuzzy Syst. 41(2), 4069–4078 (2021)

    Article  Google Scholar 

  21. Girshick R., Donahue J., Darrell T., Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Comput. Vision. Pattern. Recognit (CVPR). Preprint at https://doi.org/10.48550/arXiv.1311.2524 (2014)

  22. Girshick R. Fast R-CNN. Computer Vision and Pattern Recognition (CVPR). Preprint at https://doi.org/10.48550/arXiv.1504.08083 (2015)

  23. Ren, S.Q., He, K.M., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2015)

    Article  Google Scholar 

  24. Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: Unified, real-time object detection. Preprint at https://arxiv.org/abs/quant-ph/1506.02640v5 (2016)

  25. Redmon J., Farhadi A. Yolo9000: Better, faster, stronger. IEEE. Conf Comput Vision. Pattern. Recognit. (CVPR) 6517–6525 (2017)

  26. Redmon J., Farhadi A. Yolov3: An incremental improvement. Preprint at https://arxiv.org/abs/quant-ph/1804.02767 (2018)

  27. Wang, Z., Li, D.Y., Xu, Z., Xie, D.D.: A novel coal dust characteristic extraction to enable particle size analysis. IEEE Trans. Instrum. Meas. 70, 1557–9662 (2021)

    Google Scholar 

  28. He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  29. Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.H.: Squeezeand-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011–2023 (2017)

    Article  Google Scholar 

  30. Yu F., Koltun V. Multi-scale context aggregation by dilated convolutions. Int. Conf. Learn. Represent. Preprint at https://doi.org/10.48550/arXiv.1511.07122 (2016)

  31. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Zitnick, C.L.: Microsoft coco: common objects in context. Computer Vision and Pattern Recognition (CVPR). Preprint at https://doi.org/10.48550/arXiv.1405.0312 (2015)

    Google Scholar 

  32. Wang, Z., Zhang, H., Li, D.Y.: Feature extraction of coal dust particles in u-net network under feature compression activation. J. China. Coal. Soc. 46(9), 3056–3065 (2021)

    Google Scholar 

  33. Sun J.X. Digital image processing. Hebei Education Press (1993)

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Acknowledgements

This work is supported by National Science Foundation of China (51804249, 61603295) and Shaanxi Postdoctoral Science Foundation (2018BSHEDZZ124).

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Authors and Affiliations

  1. College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

    Hongguang Pan, Yuhong Shi, Zheng Wang & Fangfang Xin

  2. School of Artificial Intelligence, Department of Electronics and Information, Xi’an Jiaotong University, Xi’an, 710049, China

    Xinyu Lei

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  1. Hongguang Pan
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Correspondence to Hongguang Pan.

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Pan, H., Shi, Y., Lei, X. et al. Fast identification model for coal and gangue based on the improved tiny YOLO v3. J Real-Time Image Proc 19, 687–701 (2022). https://doi.org/10.1007/s11554-022-01215-1

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