Coal-Gangue Recognition Method for Top-Coal Caving Based on Improved ACGAN and PCSA-MobileNetV3

This is the authors' abstract. We don't add key points for this paper.

At the top-coal caving face, accurately and quickly identifying the content of coal and gangue is an important prerequisite for intelligent and efficient mining. Data scarcity has been caused by the limited conditions in the mine, restricting the recognition of the coal-gangue mix ratio at the top-coal caving face. Therefore, this article proposes a method for identifying the coal-gangue mix ratio under insufficient sample conditions, based on improved auxiliary classifier generative adversarial network (IACGAN) and parallel coordinate and squeeze-and-excite attention (PCSA)-MobileNetV3. First, the coal-gangue vibration data are mapped into 2-D vibration spectral images. Then, the IACGAN is enhanced with a self-attention (SA) mechanism and residual structure to generate diverse, high-quality training samples despite sample deficiency. These generated images are then added to the dataset to expand the dataset size. Finally, the PCSA mechanism is integrated into the MobileNetV3 model to classify coal-gangue vibration spectral images based on the learned spatial and spectral features. The experimental results indicate that the proposed method outperforms superior recognition performance and effectively assists in the mix ratio recognition of coal and gangue under a small sample.