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

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. 11265.rar

Coal gangue, the waste byproduct of coal mining, must be separated to improve coal quality and reduce environmental impact. Traditional manual separation is hazardous and inefficient. Modern computer vision offers a solution through deep learning, provided that robust datasets are available to handle the complex, low-light conditions of underground mines. 2. Dataset Construction and the 11,265 Samples : Salt-and-pepper noise and arithmetic mean filtering to

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 Coal gangue, the waste byproduct of coal mining,

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

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

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