The development of PlumeNet, a thermal imagery based classifier for aerosolized chemical and biological warfare agents, is detailed. PlumeNet is a convolutional neural network designed for the real-time classification of threat-like plumes from background clutter. The model weights were trained from the ground up using thermal imagery of simulant plumes recorded at various test events. The performance between different convolutional neural network architectures are compared. An analysis of the final model layers through activation mapping methods is performed to demystify the methods by which PlumeNet performs classification. The classification performance of PlumeNet at government conducted open-release field testing at Dugway Proving Ground is detailed.

Date Published: 13 May 2019
PDF: 8 pages
Proc. SPIE 10990, Computational Imaging IV, 109900L (13 May 2019); doi: 10.1117/12.2518763

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)