LWIR sensor parameters for deep learning object detectors

Robert Grimming, Bruce McIntosh, Abhijit Mahalanobis, Ronald G. Driggers

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Deep learning has been well studied for its application to image classification, object detection, and other visible spectrum tasks. However, deep learning is only beginning to be considered for applications in the long-wave infrared (LWIR) spectrum. In this work, we attempt to quantify the imaging system parameters required to perform specific deep learning tasks without significant pre-processing of the LWIR images or specialized training. We show the capabilities of uncooled microbolometer sensors for Fast Region-based Convolution Neural Networks (Fast R-CNN) object detectors and the extent to which increased sensitivity and resolution will affect a Fast R-CNN object detector's performance. These results provide guidelines for design requirements for uncooled microbolometers in industries such as commercial autonomous vehicle navigation that will use deep learning object detectors.

Original languageEnglish (US)
Pages (from-to)529-541
Number of pages13
JournalOSA Continuum
Volume4
Issue number2
DOIs
StatePublished - Feb 15 2021

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'LWIR sensor parameters for deep learning object detectors'. Together they form a unique fingerprint.

Cite this