11265.rar Apr 2026

The following is a structured paper based on the methodologies and results associated with that dataset.

The use of the expanded 11,265-sample dataset was foundational to achieving a model that is both accurate and fast enough for industrial application. Through transfer learning, the algorithm has been successfully applied to underground image segmentation, verifying its reliability as an automated solution for the coal industry.

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 ( 11265.rar

FPS increase, enabling real-time deployment on conveyor belt systems. 5. Conclusion

: 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 The following is a structured paper based on

) and real-time processing speeds, outperforming traditional YOLO architectures in underground mining environments. 1. Introduction

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 Efficient separation of coal and gangue is vital

Based on recent technical literature, the reference most likely refers to the expanded dataset used in a 2025 research study published in PLOS ONE regarding coal gangue image segmentation.