Low-resolution infrared temperature analysis for disease situation awareness via machine learning on a mobile platform

Lynne Grewe; Shivali Choudhary; Emmanuel Gallegos; Dikshant Pravin Jain; Phillip Aguilera

doi:10.1117/12.2587547

20 May 2021 Low-resolution infrared temperature analysis for disease situation awareness via machine learning on a mobile platform

Lynne Grewe, Shivali Choudhary, Emmanuel Gallegos, Dikshant Pravin Jain, Phillip Aguilera

Author Affiliations +

Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 1175614 (2021) https://doi.org/10.1117/12.2587547
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In times of health crises disease situation awareness is critical in the prevention and containment of the disease. One indicator for the development of many contagious diseases is the presence of fever and the proposed system, IRFIS, extends prior research into fever detection via infrared imaging in two key ways. Firstly, the system utilizes a modern, machine learning based object detection model for detecting heads, supplanting the traditional methods that relied upon shape matching. Secondly, IRFIS is capable of running from the Android mobile platform using a small, commercial-grade infrared camera. IRFIS’s head detection model when evaluated on a dataset of unseen images, achieved an AP of 96.7% with an IoU of 0.50 and an AR of 75.7% averaged over IoU values between 0.50 and 0.95. IRFIS calculates the target’s maximum temperature in the detected head sub-image and real results are presented as well as avenues of future work are explored.

Conference Presentation

Citation Download Citation

Lynne Grewe, Shivali Choudhary, Emmanuel Gallegos, Dikshant Pravin Jain, and Phillip Aguilera "Low-resolution infrared temperature analysis for disease situation awareness via machine learning on a mobile platform", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 1175614 (20 May 2021); https://doi.org/10.1117/12.2587547

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