Lightweight detection method of coal gangue based on multispectral and improved YOLOv5s

ABSTRACT

Aiming at the problems of poor identification effect and low separation efficiency of existing coal gangue identification and separation methods, this paper puts forward a lighter coal gangue identification and detection scheme based on YOLOv5s (You Only Look Once Version-5s) and multispectral image technology. Set up a multispectral acquisition system and shoot the required data sets. Three bands with high recognition accuracy and low correlation are selected from 25 bands in the spectrum to form RGB images for model detection. A depth separable convolution is introduced into the feature fusion network, which makes the model lighter, better fuses multiscale feature information, adds attention mechanism to the network, strengthens the objective attention, improves the ability of dense object detection and antibackground interference, reduces the complexity of the model, and improves the detection accuracy. The experimental results show that the improved YOLOv5s-MobileNet model has a mean average precision (mAP) of 98.88% on the coal and gangue test set. Compared with YOLOv5s algorithm, the model size is reduced by 12.5%, and the mAP is increased by 12.88%, which is beneficial to edge deployment to the greatest extent.

KEYWORDS:

• Object detection
• lightweight
• coal gangue identification
• Yolov5s
• attention mechanism

Acknowledgements

The authors would like to acknowledge the Open Research Grant of the Collaborative Innovation Center of Mine Intelligent Equipment and Technology (CICJMITE202203), the National Key R&D Program (2018YFC0604503), and the Anhui Post-Doctoral Research Fund (2019B350).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the Open Research Grant of Collaborative Innovation Center of Mine Intelligent Equipment and Technology under Grant CICJMITE202203, the National Key R&D Program under Grant 2018YFC0604503, and the Anhui Post-doctoral Research Fund under Grant 2019B350.