Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

• Published in: Sustainability 2023-01, Vol.15 (2), p.1041
• Main Authors: Cao, Xiangang, Zhang, Chiyu, Wang, Peng, Wei, Hengyang, Huang, Shikai, Li, Hu
• Format: Article
• Language: English
• Subjects: Behavior; Coal; Coal mines; Coal mining; Convolution; Datasets; Deep learning; Identification methods; Methods; Neural networks; Receptive field; Recognition; Sustainability
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15021041

Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional network (ST-GCN) for miners’ unsafe behavior identification network in a transportation roadway (NP-AGCN) was proposed. First, the skeleton spatial-temporal map constructed using multi-frame human key points was used for behavior recognition to reduce the interference caused by the complex environment of the coal mine. Second, aiming to solve the problem that the original graph structure cannot learn the association relationship between the non-naturally connected nodes, which leads to the low recognition rate of climbing belts, fighting and other behaviors, the graph structure was reconstructed and the original partitioning strategy was changed to improve the recognition ability of the model for multi-joint interaction behaviors. Finally, in order to alleviate the problem that the graph convolution network has difficulty learning global information due to the small receptive field, multiple self-attention mechanisms were introduced into the graph convolution to improve the recognition ability of the model for unsafe behaviors. In order to verify the detection ability of the model regarding identifying unsafe behaviors of personnel in a coal mine belt area, our model was tested on the public datasets NTU-RGB + D and the self-built datasets of unsafe behaviors in a coal mine belt area. The recognition accuracies of the proposed model in the above datasets were 94.7% and 94.1%, respectively, which were 6.4% and 7.4% higher than the original model, which verified that the proposed model had excellent recognition accuracies.