Libao Li

To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments.

This work focused on the insufficient or excessive pressure relief in large-diameter pressure relief by drilling. The influence of large-diameter pressure relief by drilling on the 6307 working face of the Tangkou coal mine on the roadway deformation was taken as the research background, with numerical simulations, indoor experiments, and on-site applications used. The influence of pressure relief drilling on roadway deformation was studied to propose segmented reaming pressure relief. The influences of parameters (e.g., reaming diameter, reaming depth, and borehole spacings) on the evolution characteristics of segmented reaming cracks and pressure relief were further investigated. The results showed that segmented reaming pressure relief reduced the roadway deformation and the peak elastic energy of coal in the impacted hazard area and improved the energy accumulation of the surrounding rocks of the roadway. The effect of segmented reaming pressure relief was positively correlated with the diameter and length of the reaming section; it was negatively correlated with borehole spacings. The optimized segmented reaming parameters of the 6307 working surface of Tangkou coal mine are as follows: the optimized reaming diameter of 240 mm, the reaming section depth of 15 m, and the borehole spacing of 1.6 m. Field tests proved that the optimized segmented reaming technology can improve the deformation of the surrounding rocks of the roadway and construction.