11265.rar Guide
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
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 Modern computer vision offers a solution through deep
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.
