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Author (up) Gkika, I.; Pattas, D.; Konstantoudakis, K.; Zarpalas, D. pdf  doi
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  Title Object detection and augmented reality annotations for increased situational awareness in light smoke conditions Type Conference Article
  Year 2023 Publication Proceedings of the 20th International ISCRAM Conference Abbreviated Journal Iscram 2023  
  Volume Issue Pages 231-241  
  Keywords Image Processing; Smoke; Augmented Reality; Deep Learning; Situational Awareness  
  Abstract Innovative technologies powered by Computer Vision algorithms can aid first responders, increasing their situ ational awareness. However, adverse conditions, such as smoke, can reduce the efficacy of such algorithms by degrading the input images. This paper presents a pipeline of image de-smoking, object detection, and augmented reality display that aims to enhance situational awareness in smoky conditions. A novel smoke-reducing deep learning algorithm is applied as a preprocessing step, before state-of-the-art object detection. The detected objects and persons are highlighted in the user’s augmented reality display. The proposed method is shown to increase detection accuracy and confidence. Testing in realistic environments provides an initial evaluation of the method, both in terms of image processing and of usefulness to first responders.  
  Address Information Technologies Institute; Information Technologies Institute; Information Technologies Institute; Information Technologies Institute  
  Corporate Author Thesis  
  Publisher University of Nebraska at Omaha Place of Publication Omaha, USA Editor Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi  
  Language English Summary Language Original Title  
  Series Editor Hosssein Baharmand Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition 1  
  ISSN ISBN Medium  
  Track Technologies for First Responders Expedition Conference  
  Notes http://dx.doi.org/10.59297/YOMA9043 Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2521  
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