Coal gangue sorting based on deep learning

Panliang Yang; Bin Zhu; Lianquan Ji; Peng Nie

doi:10.1117/12.3014357

9 January 2024 Coal gangue sorting based on deep learning

Panliang Yang, Bin Zhu, Lianquan Ji, Peng Nie

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296913 (2024) https://doi.org/10.1117/12.3014357
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Coal gangue sorting is an important link in the process of coal mining and processing, which can effectively reduce the difficulty and cost of coal post-processing. Aiming at the problems of complicated sorting process and low sorting efficiency of coal gangue, a coal gangue sorting method based on deep learning was proposed. The method is based on the YOLO v7 deep learning algorithm, and it achieves real-time detection of coal gangue by creating a coal gangue dataset and training the detection model. By constructing a coal gangue sorting platform, the capture of target gangue has been achieved. The experimental results show that the mAP of YOLO v7 model is 96.70%, and the detection speed is 69fps, which has significant advantages compared to YOLO v5, SSD and Faster RCNN algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Panliang Yang, Bin Zhu, Lianquan Ji, and Peng Nie "Coal gangue sorting based on deep learning", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296913 (9 January 2024); https://doi.org/10.1117/12.3014357

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