Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 163–170, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-163-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 163–170, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-163-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 May 2019

29 May 2019

AUTOMATIC MUCK PILE CHARACTERIZATION FROM UAV IMAGES

F. Schenk1, A. Tscharf2, G. Mayer2, and F. Fraundorfer1 F. Schenk et al.
  • 1Institute of Computer Graphics and Vision, Graz University of Technology, Austria
  • 2Chair of Mining Engineering and Mineral Economics, Montanuniversitaet Leoben, Austria

Keywords: Muck pile characterization, Fragment size distribution, Mining, Machine-learning, UAV, Computer Vision

Abstract. In open pit mining it is essential for processing and production scheduling to receive fast and accurate information about the fragmentation of a muck pile after a blast. In this work, we propose a novel machine-learning method that characterizes the muck pile directly from UAV images. In contrast to state-of-the-art approaches, that require heavy user interaction, expert knowledge and careful threshold settings, our method works fully automatically. We compute segmentation masks, bounding boxes and confidence values for each individual fragment in the muck pile on multiple scales to generate a globally consistent segmentation. Additionally, we recorded lab and real-world images to generate our own dataset for training the network. Our method shows very promising quantitative and qualitative results in all our experiments. Further, the results clearly indicate that our method generalizes to previously unseen data.