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Aerial Thermal Image based Convolutional Neural Networks for Human Detection in SubT Environments

Publicerad: 20 maj 2021

Anton Koval, Sina Sharif Mansouri, Christoforos Kanellakis, and George Nikolakopoulos

International Conference on Unmanned Aircraft Systems (ICUAS), 2021

This article proposes a novel strategy for detecting humans in harsh Sub-terranean (SubT) environments, with a thermal camera mounted on an aerial platform, based on the AlexNet Convolutional Neural Network (CNN). A transfer learning framework will be utilized for detecting the humans, where the aerial thermal images are fed to the trained network, which binary classifies them image content into two categories: a) human, and b) no human. Moreover, the AlexNet based framework is compared with two related popular CNN approaches as the GoogleNet and the Inception3Net. The efficacy of the proposed scheme has been experimentally evaluated through multiple data-sets, collected from a FLIR thermal camera during flights on an underground mining environment, fully  demonstrating the performance and merits of the proposed module.