DETERMINING ROCK FRAGMENT SIZE DISTRIBUTION USING A CONVOLUTIONAL NEURAL NETWORK

Abstract

Fast and relatively accurate determination of the fragment size distribution of a muck-pile is still a challenge in mining operations and the existing measurement methods are inefficient. In this research, a new algorithm to determine fragment size distribution due to blasting was presented, using the image processing technique. In the newly proposed approach, delineating of the fragmented rock particles, as the main core of processing, was carried out, using a convolutional neural network. Two networks were defined and trained by 150 laboratory and 150 field data images. Also, 30 laboratory and 30 field data images were applied to carry out the validation visually, and by using F1-scores. For the two laboratory and field networks and results obtained by Split-Desktop software automatic edge detection on the same images, the F1-scores are equal to (0.98, 0.74) and (0.99, 0.85) respectively. Also, for determination of fragment size distribution by laboratory data network and Split-Desktop software automatic edge detection on the same images, the Root Mean Square Error (RMSE) for F30 and F80 are equal to (0.36, 1.20) and (0.31, 1.24) respectively. These indicate better performance of the proposed approach for both rock edge detection and fragment size distribution over Split-Desktop software automatic edge detection.

Keywords

• image processing
• rock edge detection
• determination of fragments size distribution
• machine learning
• convolutional neural networks