Fast Rock Segmentation Using Artificial Intelligence

"Image-based rock fragmentation sensing in mines and quarries includes an important rock boundary delineation step, commonly referred to as rock segmentation. This paper presents an artificial intelligence-based solution to this challenge. The proposed technique encodes prior knowledge of previously analyzed images into mathematical/statistical models. Human-labeled images are used as inputs to train neural networks through an optimization process. The networks can then be used in real time for rock delineation. To build the models, special deep artificial neural networks are used as a pixel classifier. The proposed classifier labels each pixel (edge, rock or fine) by analyzing a plurality of pixels within the image. Advances in machine learning allow the network to contain many parameters. The increased number of parameters is a strong factor in the classifier’s ability to correctly classify each pixel. Deep learning-based segmentation, combined with 3D imaging followed by post processing, provides a unified fragmentation sensing solution. Results from automatic segmentation are compared to human labeled segmentation using the percentage passing curves for 64 rock images. INTRODUCTION Mines and quarries can see significant productivity and performance benefits by controlling material sizes (McKee, Chitmobo, & Morrell, 1995; Sellers & Gumede, 2012). During the past decade, image-based fragmentation analysis has been applied to estimate rock size distributions in order to optimize procedures in mines and quarries. Using a roving camera and operator assisted analysis Maerz, Franklin, and Coursen (1987) measured the size distribution of the blasted rock and eliminated the need for manual sieve analysis. Since then, many others have introduced automated and manual methods for fragmentation analysis and improved upon existing approaches (Girdner, Kemeny, Srikant, & McGill, 1996; Smith & Kemeny, 1993; Palangio, Palangio, & Maerz, 2005; Tafazoli & Ziraknejad, 2009; Raina, 2013). In image-based fragmentation, rock boundaries are identified in the image, and an image scaling is applied to transform rock pixel sizes into real world dimensions. Usually geometric references, such as regularly shaped objects (discs and basketballs) (Siddigui, Ali Shah, & Behan, 2009), or the known size of an excavator bucket (Zeng, Chow, Baumann, & Tafazoli, 2012), are used to determine the proper scaling factor. In recent years, 3D imaging and sensing have also been incorporated to improve rock delineation. Specific methods include using camera-laser combinations and stereo imaging to measure rock fragmentation on conveyor belts (Noy, 2013; Thurley, 2013; Dislaire, Illing, Laurent, & Pirard, 2013) as well as portable fragmentation analysis devices with 3D imaging sensors (Sameti, et al., 2014)."