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  4. Vol 6, Issue 5, 2022

Coal Gangue Identification Based on Improved YOLOv5s Algorithm

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DOI: 10.23977/acss.2022.060512 | Downloads: 17 | Views: 570

Author(s)

Shunan Jia 1

Affiliation(s)

1 School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China

Corresponding Author

Shunan Jia

ABSTRACT

For coal gangue sorting task, most of them are still in the traditional gangue sorting method, for this situation, based on the analysis and summary of previous research work, a coal For coal gangue sorting task, most of them are still in the traditional gangue sorting method, for this situation, based on the analysis and summary of previous research work, a coal gangue identification method based on improved YOLOv5s is proposed. On the basis of the original network model to make improvements in the backbone module to add non-local attention mechanism for multi-scale network prediction of gangue, improve the feature extraction ability of the model; and design adaptive anchor frame to predict gangue location information efficiently; improve the loss function of the original model to reduce the target miss detection, so as to effectively improve the accuracy of gangue identification. Finally, through experimental comparison, compared with the original network, the improved YOLOv5s model has an mAP value of 8.7%, and the detection speed has been greatly improved.

KEYWORDS

YOLOv5s, gangue, image recognition

CITE THIS PAPER

Shunan Jia, Coal Gangue Identification Based on Improved YOLOv5s Algorithm. Advances in Computer, Signals and Systems (2022) Vol. 6: 84-93. DOI: http://dx.doi.org/10.23977/acss.2022.060512.

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