) 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
: 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 11265.rar
Deep Learning-Based Segmentation of Coal Gangue: An Improved YOLOv8 Approach Using the 11,265 Image Dataset
The following is a structured paper based on the methodologies and results associated with that dataset. Key hyperparameters were adjusted to better suit the
FPS increase, enabling real-time deployment on conveyor belt systems. 5. Conclusion
The model trained on the showed significant performance gains over previous iterations: Accuracy (Precision) : improvement over standard models). Recall : Mean Average Precision (mAP) : Inference Speed : 32.1132.11 frames per second (FPS), representing an the model achieves high precision (
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 (