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) are 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 took place. CFSs are usually conducted using manual data collection methods, which may pose the personnel to some risks by being subjected to road traffic and different weather conditions for several hours. On other hand, the quality and accuracy of the collected HAZMAT data is impacted 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 Network Video Recorder (NVR) mounted on a mobile trailer. The developed system was used as the incessant data collection system. Manual data collection was also conducted at the same locations to calibrate and validate the new developed system. The results showed that the proposed framework could achieve an accuracy of 95% to identify HAZMAT placards 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.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; Photos; References;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01763629
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03863
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:07AM