Automatic Muck Pile Characterization from UAV Images

Fabian Schenk, Alexander Tscharf, Gerhard Mayer, Friedrich Fraundorfer

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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.
Originalspracheenglisch
TitelSPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Seitenumfang8
PublikationsstatusEingereicht - 10 Jun 2019
VeranstaltungISPRS Geospatial Week: Unmanned Aerial Vehicles in Geomatics (UAVg) 2019 - University of Twente, Enschede, Niederlande
Dauer: 10 Jun 201914 Jun 2019
http://www.uav-g.com/

Konferenz

KonferenzISPRS Geospatial Week
LandNiederlande
OrtEnschede
Zeitraum10/06/1914/06/19
Internetadresse

Fingerprint

Unmanned aerial vehicles (UAV)
Piles
Open pit mining
Learning systems
Masks
Scheduling
Processing
Experiments

Schlagwörter

    Dies zitieren

    Schenk, F., Tscharf, A., Mayer, G., & Fraundorfer, F. (2019). Automatic Muck Pile Characterization from UAV Images. Manuskript zur Veröffentlichung eingereicht. in SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

    Automatic Muck Pile Characterization from UAV Images. / Schenk, Fabian; Tscharf, Alexander; Mayer, Gerhard; Fraundorfer, Friedrich.

    SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Schenk, F, Tscharf, A, Mayer, G & Fraundorfer, F 2019, Automatic Muck Pile Characterization from UAV Images. in SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences., Enschede, Niederlande, 10/06/19.
    Schenk F, Tscharf A, Mayer G, Fraundorfer F. Automatic Muck Pile Characterization from UAV Images. in SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019
    Schenk, Fabian ; Tscharf, Alexander ; Mayer, Gerhard ; Fraundorfer, Friedrich. / Automatic Muck Pile Characterization from UAV Images. SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019.
    @inproceedings{87d1dd2c7d624b5693d58454fc58f66c,
    title = "Automatic Muck Pile Characterization from UAV Images",
    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.",
    keywords = "UAVs, Semantic Segmentation, Mining, Machine learning, Convolutional neural networks",
    author = "Fabian Schenk and Alexander Tscharf and Gerhard Mayer and Friedrich Fraundorfer",
    year = "2019",
    month = "6",
    day = "10",
    language = "English",
    booktitle = "SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

    }

    TY - GEN

    T1 - Automatic Muck Pile Characterization from UAV Images

    AU - Schenk, Fabian

    AU - Tscharf, Alexander

    AU - Mayer, Gerhard

    AU - Fraundorfer, Friedrich

    PY - 2019/6/10

    Y1 - 2019/6/10

    N2 - 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.

    AB - 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.

    KW - UAVs

    KW - Semantic Segmentation

    KW - Mining

    KW - Machine learning

    KW - Convolutional neural networks

    M3 - Conference contribution

    BT - SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

    ER -