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.

Research on coal foreign object detection based on deep learning is of great significance to safe, efficient, and green production of coal mines. However, the foreign object image dataset is scarce due to collection conditions, which brings an enormous challenge to coal foreign object detection. To achieve augmentation of foreign object datasets, a high-quality coal foreign object image generation method based on improved StyleGAN is proposed. Firstly, the dual self-attention module is introduced into the generator to strengthen the long-distance dependence of features between spatial and channel, refine the details of the generated images, accurately distinguish the front background information, and improve the quality of the generated images. Secondly, the depthwise separable convolution is introduced into the discriminator to solve the problem of low efficiency caused by the large number of parameters of multi-stage convolutional networks, to realize the lightweight model, and to accelerate the training speed. Experimental results show that the improved model has significant advantages over several classical GANS and original StyleGAN in terms of quality and diversity of the generated images, with an average improvement of 2.52 in IS and a decrease of 5.80 in FID for each category. As for the model complexity, the parameters and training time of the improved model are reduced to 44.6% and 58.8% of the original model without affecting the generated images quality. Finally, the results of applying different data augmentation methods to the foreign object detection task show that our image generation method is more effective than the traditional methods, and that, under the optimal conditions, it improves AP_box by 5.8% and AP_mask by 4.5%.