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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

Deep Learning-Based Segmentation of Coal Gangue: An Improved YOLOv8 Approach Using the 11,265 Image Dataset 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 The model trained on the showed significant performance

) and real-time processing speeds, outperforming traditional YOLO architectures in underground mining environments. 1. Introduction Modern computer vision offers a solution through deep

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.

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