Detection of coal gangue by YOLO deep learning method based on channel pruning

ABSTRACT

Rapid and accurate identification of coal and gangue is of great significance to improve sorting efficiency and reduce environmental pollution. Image recognition based on deep learning is the current research hotspot, but most of the existing deep learning network models for coal gangue recognition are complex in structure and rely on high-performance computing resources, which is difficult to deploy in edge devices. To resolve this problem, based on the YOLOv5s model and on the condition of ensuring the accuracy of coal gangue identification, this paper uses channel pruning and transfer learning technology to realize the lightweight of the coal gangue identification and detection model. Results show that the model size is significantly reduced by this method. The parameter and the amount of computation were reduced by 78.7% and 62%, and the reasoning speed was 6.8 ms per image. The superiority of this model is further verified by comparing it with 7 mainstream target detection networks. The proposed lightweight treatment method of coal gangue identification model effectively reduces the model volume and improves the real-time identification.

KEYWORDS:

• YOLOv5s
• coal gangue identification
• channel pruning
• lightweight
• real-time detection

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the General supported projects of National Natural Science Foundation of China [52174154].