An Underground Abnormal Behavior Recognition Method Based on an Optimized Alphapose-ST-GCN

Author:

Shi Xiaonan1ORCID,Huang Jian2,Huang Bo1

Affiliation:

1. College of Computer Science and Technology, Xi’an University of Science and Technology, 58 Yan ta road, Xi’an, Shaanxi 710054, P. R. China

2. Computer School, Hubei University of Arts and Science, 296 Long Zhong Road, Xiang Yang, Hubei 441000, P. R. China

Abstract

Due to the complex underground environment of coal mines, the unsafe behaviors of miners are likely to lead safety accidents. Therefore, research on underground abnormal behavior recognition methods based on video images is gradually gaining attention. This paper proposes an underground abnormal behavior recognition method based on an optimized Alphapose-ST-GCN. First, an image set captured in underground monitoring video is defogged and enhanced by the CycleGAN. Second, the Alphapose target detection is optimized using the LTWOA-Tiny-YOLOv3 model. Third, the ST-GCN is used for abnormal behavior recognition. The image quality of the dataset before and after a CycleGAN enhancement is compared, the convergence curves of LTWOA under four test functions are compared, and the mean average accuracy mAP of the LTWOA-Tiny-YOLOv3 model is evaluated. Finally, the performance of the proposed method is compared with other detection algorithms. The results show that CycleGAN significantly improves the quality of the dataset images. The whale optimization algorithm improved by the logistic-tent chaos mapping has a more significant convergence effect than the other optimization algorithms, and the LTWOA-Tiny-YOLOv3 model has a better recognition accuracy of 9.1% in mAP compared with the unoptimized model. The underground abnormal detection model proposed in this paper achieves an 82.3% accuracy on the coal mine underground behavior dataset.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture

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