Document Type : Research Paper
Mining Engineering Department, Shahid Bahonar University of Kerman
Mining Engineering, Department of Mining Engineering, Shahid Bahonar University of Kerman
Mining Engineering, Department of Mining Engineering, Shahid Bahonar University of Kerman, Iran
Department of Electrical Engineering, Shahid Bahonar University of Kerman
Muck-pile size distribution is one of the most important parameters in open pit blasting that can affect mining and mineral processing efficiency. For evaluating fragmentation by blasting, digital image analysis is a fast and reliable indirect technique. In this study, based on neural network and visual feature extractions, an algorithm was developed to determine muck-piles size distribution using digital images.26 test images of fragmented rockwere used to determine size distribution and the results were compared with the results of automatic and manual aged detection of Split-Desktop software.The results showeda general improvement in evaluating rockparticles size distribution. We obtained an improvement of 67%, 57% and 28%, respectively using Fourier transform, Gabor and wavelet methods. Fourier transform, Gabor and wavelet methods showed also an improvement of 52%, 40% and 21 %, respectively in evaluating of F10 to F50.