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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

A critical challenge in training neural networks for mining is the lack of diverse data. In the primary study, an initial set of 1,980 original images was collected. To improve generalization and prevent overfitting, various were applied: Geometric Transformations : Image rotation (randomly ±90plus or minus 90 Photometric Adjustments : Random luminance changes (up to ) to simulate varying lighting underground.

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

: 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 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. Coal gangue, the waste byproduct of coal mining,

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

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 ( ) and real-time processing speeds

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