Efficient separation of coal and gangue is vital for sustainable mining. This paper details the development of an improved YOLOv8 model for image segmentation, trained on a comprehensive dataset expanded to images. By utilizing data expansion techniques and transfer learning, the model achieves high precision (
Deep Learning-Based Segmentation of Coal Gangue: An Improved YOLOv8 Approach Using the 11,265 Image Dataset 11265.rar
) and real-time processing speeds, outperforming traditional YOLO architectures in underground mining environments. 1. Introduction Efficient separation of coal and gangue is vital
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