Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network
Conducting hazardous materials (HAZMAT) commodity flow studies (CFS) is crucial for emergency management agencies. Identifying the types and amounts of hazardous materials being transported through a specified geographic area will ensure timely response if a HAZMAT incident takes place. CFS are usually conducted using manual data collection methods, which may expose the personnel to some risks by them being subjected to road traffic and different weather conditions for several hours. On other hand, the quality and accuracy of the collected HAZMAT data are affected by the skill and alertness of the data collectors. This study introduces a framework to collect HAZMAT transportation data exploiting advanced image processing and machine learning techniques on video feed. A promising convolutional neural network (CNN), named AlexNet was used to develop and test the automatic HAZMAT placard recognition framework. A solar-powered mobile video recording system was developed using high-resolution infra-red (IR) cameras, connected to a network video recorder (NVR) mounted on a mobile trailer. The developed system was used as the continuous data collection system. Manual data collection was also conducted at the same locations to calibrate and validate the newly developed system. The results showed that the proposed framework could achieve an accuracy of 95% in identifying HAZMAT placard information. The developed system showed significant benefits in reducing the cost of conducting HAZMAT CFS, as well as eliminating the associated risks that data collection personnel could face.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- Sherif Gaweesh https://orcid.org/0000-0001-7977-6378 © National Academy of Sciences: Transportation Research Board 2021.
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Authors:
- Gaweesh, Sherif
- 0000-0001-7977-6378
- Khan, Md Nasim
- 0000-0001-5996-091X
- Ahmed, Mohamed M
- 0000-0002-1921-0724
- Publication Date: 2021-11
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; Photos; References;
- Pagination: pp 1357-1371
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2675
- Issue Number: 11
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Commodity flow; Data collection; Freight transportation; Hazardous materials; Image processing; Machine learning; Neural networks; Pattern recognition systems; Placarding; Traffic safety; Videotape recorders
- Subject Areas: Data and Information Technology; Environment; Freight Transportation; Highways; Materials; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01763629
- Record Type: Publication
- Report/Paper Numbers: TRBAM-21-03863
- Files: TRIS, TRB, ATRI
- Created Date: Feb 4 2021 10:57AM