A Computer Vision Application in Real-time to Identifying Big Rocks with Applications to the Mining Industry

Detection of big rocks is an important, even critical, problem in the mining industry because they could block several machines and this causes high costs. This paper presents a computer-vision-based method to detect big rocks in a real mining industry. Our system, based on a mixture of image processing techniques and neural networks, works as follows: once the image is taken, a pre-processing step is performed, filtering the image and extracting a set of candidate rocks. Then a neural network processes the candidate rocks to ensure correct detection. A tracking algorithm is then applied to avoid false detection due to rock grouping. Using geometrical information, it is possible to estimate the real dimensions of the rocks. Our computer vision system satisfies time constraints imposed by the industry to work in real time and is currently operating. The algorithm presented is independent of the rock’s shape. Results obtained during nine months of unsupervised work are provided, showing that our system is able to work under different light conditions and is robust enough to face real work conditions.