Hybrid connected attentional lightweight network for gangue intelligent segmentation in top-coal caving face

Abstract

The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset.

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

“…Depending on the different frequency ranges of the electromagnetic waves, ground-penetrating radar [25][26][27], terahertz signals [28,29], and electron resonance identification [30] are additional modalities of detection. In the context of coal caving, numerous scholars have introduced and experimented with various techniques involving radiation [31], visuals [32,33], vibrations [34,35], sounds [36], and infrared spectroscopy recognition [37][38][39] to accurately identify the realtime caving status of coal and gangue. These technological principles have provided valuable experience for the automated control of top coal caving in LTCC mining.…”