Hazmat label recognition and localization for rescue robots in disaster scenarios

Raimund Edlinger; Michael Zauner; Gerald Zauner

doi:10.2352/ISSN.2470-1173.2019.7.IRIACV-463

Hazmat label recognition and localization for rescue robots in disaster scenarios

Raimund Edlinger, Michael Zauner, Gerald Zauner

Research Center Wels

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution

5 Citations (Scopus)

Abstract

Firefighting and rescue live victims operations are inherently dangerous, but the imminent danger of release of a hazardous substance creates an additional risk. Thus, identification of hazardous materials during robot assisted search and rescue missions can help e.g. firefighters or rescue teams to improve such rescue operations. The paper deals with the development of such a robotic machine vision system for hazmat label recognition. Classical computer vision methods but also state-of-the-art deep learning based detection algorithms were implemented and evaluated. Special focus was put on the robustness of detection and recognition with limited hardware resources and the influence of background image structures and light conditions.

Original language	English
Title of host publication	IS&T Electronic Imaging 2019
Volume	2019
Edition	7
DOIs	https://doi.org/10.2352/ISSN.2470-1173.2019.7.IRIACV-463
Publication status	Published - 13 Jan 2019
Event	Intelligent Robotics and Industrial Applications using Computer Vision 2019 - San Francisco, United States Duration: 13 Jan 2019 → 17 Jan 2019 https://www.imaging.org/site/IST/Conferences/EI/EI_2019/Conference/C_IRIACV.aspx

Publication series

Name	IS and T International Symposium on Electronic Imaging Science and Technology

Conference

Conference	Intelligent Robotics and Industrial Applications using Computer Vision 2019
Country/Territory	United States
City	San Francisco
Period	13.01.2019 → 17.01.2019
Internet address	https://www.imaging.org/site/IST/Conferences/EI/EI_2019/Conference/C_IRIACV.aspx

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10.2352/ISSN.2470-1173.2019.7.IRIACV-463

Cite this

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title = "Hazmat label recognition and localization for rescue robots in disaster scenarios",

abstract = "Firefighting and rescue live victims operations are inherently dangerous, but the imminent danger of release of a hazardous substance creates an additional risk. Thus, identification of hazardous materials during robot assisted search and rescue missions can help e.g. firefighters or rescue teams to improve such rescue operations. The paper deals with the development of such a robotic machine vision system for hazmat label recognition. Classical computer vision methods but also state-of-the-art deep learning based detection algorithms were implemented and evaluated. Special focus was put on the robustness of detection and recognition with limited hardware resources and the influence of background image structures and light conditions.",

author = "Raimund Edlinger and Michael Zauner and Gerald Zauner",

year = "2019",

month = jan,

day = "13",

doi = "10.2352/ISSN.2470-1173.2019.7.IRIACV-463",

language = "English",

volume = "2019",

series = "IS and T International Symposium on Electronic Imaging Science and Technology",

booktitle = "IS&T Electronic Imaging 2019",

edition = "7",

note = "Intelligent Robotics and Industrial Applications using Computer Vision 2019 ; Conference date: 13-01-2019 Through 17-01-2019",

url = "https://www.imaging.org/site/IST/Conferences/EI/EI_2019/Conference/C_IRIACV.aspx",

}

Edlinger, R , Zauner, M & Zauner, G 2019, Hazmat label recognition and localization for rescue robots in disaster scenarios. in IS&T Electronic Imaging 2019. 7 edn, vol. 2019, IS and T International Symposium on Electronic Imaging Science and Technology, Intelligent Robotics and Industrial Applications using Computer Vision 2019, San Francisco, United States, 13.01.2019. https://doi.org/10.2352/ISSN.2470-1173.2019.7.IRIACV-463

TY - GEN

T1 - Hazmat label recognition and localization for rescue robots in disaster scenarios

AU - Edlinger, Raimund

AU - Zauner, Michael

AU - Zauner, Gerald

PY - 2019/1/13

Y1 - 2019/1/13

N2 - Firefighting and rescue live victims operations are inherently dangerous, but the imminent danger of release of a hazardous substance creates an additional risk. Thus, identification of hazardous materials during robot assisted search and rescue missions can help e.g. firefighters or rescue teams to improve such rescue operations. The paper deals with the development of such a robotic machine vision system for hazmat label recognition. Classical computer vision methods but also state-of-the-art deep learning based detection algorithms were implemented and evaluated. Special focus was put on the robustness of detection and recognition with limited hardware resources and the influence of background image structures and light conditions.

AB - Firefighting and rescue live victims operations are inherently dangerous, but the imminent danger of release of a hazardous substance creates an additional risk. Thus, identification of hazardous materials during robot assisted search and rescue missions can help e.g. firefighters or rescue teams to improve such rescue operations. The paper deals with the development of such a robotic machine vision system for hazmat label recognition. Classical computer vision methods but also state-of-the-art deep learning based detection algorithms were implemented and evaluated. Special focus was put on the robustness of detection and recognition with limited hardware resources and the influence of background image structures and light conditions.

UR - http://www.scopus.com/inward/record.url?scp=85080877980&partnerID=8YFLogxK

U2 - 10.2352/ISSN.2470-1173.2019.7.IRIACV-463

DO - 10.2352/ISSN.2470-1173.2019.7.IRIACV-463

M3 - Conference contribution

VL - 2019

T3 - IS and T International Symposium on Electronic Imaging Science and Technology

BT - IS&T Electronic Imaging 2019

T2 - Intelligent Robotics and Industrial Applications using Computer Vision 2019

Y2 - 13 January 2019 through 17 January 2019

ER -

Hazmat label recognition and localization for rescue robots in disaster scenarios

Abstract

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Conference

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