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A critical challenge in training neural networks for mining is the lack of diverse data. In the primary study, an initial set of 1,980 original images was collected. To improve generalization and prevent overfitting, various were applied: Geometric Transformations : Image rotation (randomly ±90plus or minus 90 Photometric Adjustments : Random luminance changes (up to ) to simulate varying lighting underground.

The research implemented an "improved YOLOv8" model, specifically optimized for segmentation rather than just object detection. Key hyperparameters were adjusted to better suit the morphology of coal and rock. 4. Results and Performance 11265.rar

: Salt-and-pepper noise and arithmetic mean filtering to mimic camera sensor interference.Through these methods, the dataset was expanded to a total of 11,265 pieces of gangue samples, providing the necessary volume for high-accuracy training. 3. Model Architecture: Improved YOLOv8 A critical challenge in training neural networks for

Deep Learning-Based Segmentation of Coal Gangue: An Improved YOLOv8 Approach Using the 11,265 Image Dataset 265 pieces of gangue samples