A fast gangue detection algorithm based on multi-head self-attention mechanism and anchor frame optimization strategy

ABSTRACT

Because of the problem of difficult identification, model acceleration, and localization for coal gangue detection, a fast coal gangue detection algorithm based on a multi-head self-attention mechanism and anchor frame optimization strategy is designed. Firstly, the multi-head self-attention mechanism MHSA is combined with the CNN backbone structure of YOLOv5s, which is used to enhance the algorithm’s ability to perceive global information to strengthen the model’s recognition of coal and gangue; Secondly, the lightweight Ghost module will be used to reconfigure the backbone of YOLOv5s and the feature fusion layer to reduce the complexity of the model and increase the speed of the coal gangue image detection; And lastly, the selection of an appropriate strategy of input image scale and anchor frame size to improve the performance of the detection algorithm to solve the problem of localization of coal and gangue. Experiments show that the average accuracy of the improved MYL model mAP_0.5 reaches 91.8%, the detection accuracy of mAP_0.5 is improved by 3.6%, and the detection speed is 80 FPS. The improved MYL model can show stronger robustness and superior performance in the detection of coal gangue and can meet the requirements of effective industrial detection.

KEYWORDS:

• Gangue
• YOLOv5s
• MHSA
• Anchor frame optimization strategy

Disclosure statement

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

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Ethics Note

Consent was sought from each author and the subjects who provided data for the dataset in this paper, and no ethics were involved in the rest of the paper.

Additional information

Funding

This research was funded by the Foundation of the National Natural Science Foundation of China [No. 61903126]. We wish to express our sincere thanks to thank the anonymous reviewers for their valuable suggestions and comments regarding this paper in this paper. We would also like to moreover, we thank the author who provided the reference code for this article.