The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 811–817, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-811-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 811–817, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-811-2019

  05 Jun 2019

05 Jun 2019

INDOOR 3D INTERACTIVE ASSET DETECTION USING A SMARTPHONE

R. Kostoeva, R. Upadhyay, Y. Sapar, and A. Zakhor R. Kostoeva et al.
  • Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA

Keywords: Asset Management, Asset Detection, Asset Recognition, Asset Localization, Augmented Reality, Building Information Modelling, Mobile Mapping, Indoor Mapping

Abstract. Building floor plans with locations of safety, security and energy assets such as IoT sensors, thermostats, fire sprinklers, EXIT signs, fire alarms, smoke detectors, routers etc. are vital for climate control, emergency security, safety, and maintenance of building infrastructure. Existing approaches to building survey are manual, and usually involve an operator with a clipboard and pen, or a tablet enumerating and localizing assets in each room. In this paper, we propose an interactive method for a human operator to use an app on a smart phone to (a) create the 2D layout of a room, (b) detect assets of interest, and (c) localize them within the layout. We use deep learning methods to train a neural network to recognize assets of interest, and use human in the loop interactive methods to correct erroneous recognitions by the networks. These corrections are then used to improve the accuracy of the system over time as the inspector moves from one room to another in a given building or from one building to the next; this progressive training and testing mechanism makes our system useful in building inspection scenarios where a given class of assets in a building are same instantiation of that object category, thus reducing the problem to instance, rather than category recognition. Experiments show our proposed method to achieve accuracy rate of 76% for testing 102 objects across 10 classes.