Classification and object detection with image assisted total station and machine learning
Author:
Affiliation:
1. Institute for Spatial Information and Surveying Technology , i3mainz, Mainz University of Applied Sciences , Lucy-Hillebrand-Straße 2, 55128 Mainz , Germany
Abstract
Funder
Carl-Zeiss-Stiftung
Publisher
Walter de Gruyter GmbH
Subject
Earth and Planetary Sciences (miscellaneous),Engineering (miscellaneous),Modeling and Simulation
Link
https://www.degruyter.com/document/doi/10.1515/jag-2023-0011/pdf
Reference30 articles.
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2. Zschiesche, K. Image assisted total stations for structural health monitoring—a review. Geomatics 2022;2:1–16. https://doi.org/10.3390/geomatics2010001.
3. Zschiesche, K, Fitzke, M, Schlüter, M. Self-calibration and crosshair tracking with modular digital imaging total station. J Photogramm Remote Sens Geoinf Sci 2022;90:543–57. https://doi.org/10.1007/s41064-022-00220-0.
4. Bürki, B, Guillaume, S, Sorber, P, Oesch, HP. DAEDALUS: a versatile usable digital clip-on measuring system for total stations. In: IEEE, 2010 international conference on indoor positioning and indoor navigation (IPIN 2010); 2010:1–10 pp.
5. Guillaume, S, Bürki, B, Griffet, S, Durand, HM. QDaedalus: augmentation of total stations by CCD sensor for automated contactless high-precision metrology. In: FIG working week 2012; 2012.
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