Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field

Alexander Pichler; Nicolas Hueber; Christophe Hennequin

doi:10.1117/12.2639208

2 November 2022 Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field

Alexander Pichler, Nicolas Hueber, Christophe Hennequin

Author Affiliations +

Proceedings Volume 12272, Electro-Optical Remote Sensing XVI; 1227209 (2022) https://doi.org/10.1117/12.2639208
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Performing specific object detection and recognition at the imaging sensor level, raises many technical and scientific challenges. Today state-of-the-art detection performances are obtained with Deep Convolutional Neural Network (CNN) models. However reaching the expected CNN behavior in terms of sensitivity and specificity require to master the training dataset. We explore in this paper, a new way of acquiring images of military vehicles in sanitized and controlled conditions of the laboratory in order to train a CNN to recognize the same visual signature with real vehicles in realistic outdoor situations. By combining sanitized images, counter-examples and different data augmentation techniques, our investigations aim at reducing the needs of complex outdoor image acquisition. First results demonstrate the feasibility to detect and classify, in real situations, military vehicles by exploiting only signatures from miniature models.

Conference Presentation

Citation Download Citation

Alexander Pichler, Nicolas Hueber, and Christophe Hennequin "Investigating sanitized and controlled image dataset to train deep convolutional neural networks for remote object detection on the field", Proc. SPIE 12272, Electro-Optical Remote Sensing XVI, 1227209 (2 November 2022); https://doi.org/10.1117/12.2639208

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available