A novel deep learning model based on YOLOv5 optimal method for coal gangue image recognition.

Journal: Scientific reports
Published Date: May 28, 2025

Abstract

Coal gangue recognition presents significant challenges in the mining industry due to its inefficient and costly traditional treatment methods. The advent of deep learning techniques has introduced novel solutions for automating and online coal gangue processing. Despite the potential of deep learning models, challenges such as overfitting and the need for extensive labeled datasets hinder their effectiveness. You Only Look Once version 5 (YOLOv5), with its rapid inference speed and high accuracy, offers a suitable solution for real-time coal gangue detection. This research investigates the application of YOLOv5 for coal gangue image recognition, involving data preprocessing, model training, and optimization. Experimental results demonstrate that incorporating the multiple channel attention mechanism and lightweight content-aware re-assembly of features up-sampling operator significantly improves model confidence and recognition performance.

Authors

  • Tongkai Gu
    School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
  • Haiyan Zhao
    Key Laboratory for Colloid and Interface Chemistry of Education Ministry, School of Chemistry and Chemical Engineering, Shandong University, 250100 Jinan, PR China.
  • Yasheng Chang
    School of Optical and Electronic Information & Suzhou Key Laboratory of Biophotonics & International Joint Metacenter for Advanced Photonics and Electronics, Suzhou City University, Suzhou, 215104, China. cocys@126.com.
  • Sitong Yan
    School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
  • Feihan Cao
    School of Optical and Electronic Information & Suzhou Key Laboratory of Biophotonics & International Joint Metacenter for Advanced Photonics and Electronics, Suzhou City University, Suzhou, 215104, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.

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