Coal and gangue recognition technology is one of the key technologies in the intelligent construction of coal mines. With the deepening of the research, only the coal and gangue recognition, the pixel segmentation of the coal gangue image is needed. Aiming at the gangue segmentation algorithm with low accuracy, easy to miss detection, wrong detection and large amount of detection data, slow detection speed and other problems. A coal gangue segmentation model based on improved YOLOv8 is proposed to achieve fast and accurate recognition of coal gangue images, and the overall computational volume of the model is not large, which has achieved better application results. Using the YOLOv8 model as the base model, the standard convolutional modules in the first, second & third C2f modules were replaced with depth separable convolution (DSC) modules in the YOLOv8 model backbone network, reducing the overall computational effort of the model. Adding the CBAM module before the second convolution of the up-sampling module and down-sampling stage in the model neck network improves the differentiation of the model for gangue and enhances the recognition accuracy. The original dataset was expanded from 1980 to 11,265 sheets using data expansion techniques and some hyperparameters were adjusted. Results show that the improved YOLOv8 model has an accuracy (Precision) of 95.67%, a recall (Recall) of 95.74%, a transmitted frames per second (FPS) of 32.11 frames/s, and a mean average precision (mAP) of 96.88%, which is an improvement of 5.6% in accuracy, 7.12% in recall, and the mean average precision (mAP) is improved by 4.65% and FPS is improved by 8.83 frames/s. By comparing with YOLOv3, YOLOv5, YOLOv7, and YOLOv8 models, the improved model is optimal in terms of accuracy and speed. Finally, the model is successfully applied to underground coal gangue image segmentation through transfer learning, and the effect of coal gangue image segmentation is good, which verifies the re-liability of the algorithm. [ABSTRACT FROM AUTHOR]

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