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Author  |
Gkika, I.; Pattas, D.; Konstantoudakis, K.; Zarpalas, D. |
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Title |
Object detection and augmented reality annotations for increased situational awareness in light smoke conditions |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
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Volume |
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Pages |
231-241 |
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Keywords |
Image Processing; Smoke; Augmented Reality; Deep Learning; Situational Awareness |
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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. |
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Address |
Information Technologies Institute; Information Technologies Institute; Information Technologies Institute; Information Technologies Institute |
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Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
Hosssein Baharmand |
Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
1 |
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ISSN |
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ISBN |
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Track |
Technologies for First Responders |
Expedition |
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Conference |
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Notes |
http://dx.doi.org/10.59297/YOMA9043 |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2521 |
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