A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract)

Authors

  • Junchi Bin University of British Columbia, Okanagan Campus
  • Choudhury A. Rahman Intelliview Technologies Inc.
  • Shane Rogers Intelliview Technologies Inc.
  • Shan Du University of British Columbia, Okanagan Campus
  • Zheng Liu University of British Columbia, Okanagan Campus

DOI:

https://doi.org/10.1609/aaai.v36i11.21595

Keywords:

Visual Surveillance, Multimodal Modeling, Infrared Image Processing, Multimodal Object Detection, Industrial Applications, Sensor Fusion

Abstract

Fossil energy products such as liquefied natural gas (LNG) are among Canada's most important exports. Canadian engineers devote themselves to constructing visual surveillance systems for detecting potential LNG emissions in energy facilities. Beyond the previous infrared (IR) surveillance system, in this paper, a multimodal fusion-based LNG detection (MFLNGD) framework is proposed to enhance the detection quality by the integration of IR and visible (VI) cameras. Besides, a Fourier transformer is developed to fuse IR and VI features better. The experimental results suggest the effectiveness of the proposed framework.
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Published

2022-06-28

How to Cite

Bin, J., Rahman, C. A., Rogers, S., Du, S., & Liu, Z. (2022). A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12917-12918. https://doi.org/10.1609/aaai.v36i11.21595

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program