Presentation + Paper
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 +
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
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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KEYWORDS
Video

Image processing

Image quality

Cameras

Databases

Data acquisition

Data modeling

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