11265.rar Apr 2026

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.

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 11265.rar

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 ( Based on recent technical literature, the reference most

: 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 This paper details the development of an improved

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

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.