Automated method for generating datasets of infrared or visible images for context-specific training of deep neural network-based object detectors

Alexander Pichler; Nicolas Hueber

doi:10.1117/12.2684062

19 October 2023 Automated method for generating datasets of infrared or visible images for context-specific training of deep neural network-based object detectors

Alexander Pichler, Nicolas Hueber

Author Affiliations +

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330I (2023) https://doi.org/10.1117/12.2684062
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Performing object detection and recognition at the imaging sensor level, raises many technical and scientific challenges. Today’s 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 fast and automated method to acquire images of vehicles in infrared and visible range employing a commercial inspection drone equipped with thermal and visible range cameras, associated to a dedicated data-augmentation method for automated generation of context-specific machine learning datasets. The purpose is to successfully train a CNN to recognize the vehicles in realistic outdoor situations in infrared or visible range images, while reducing mandatory access to the vehicles of interest and the needs of complex and long outdoor image acquisition. First results demonstrate the feasibility of our approach for training a deep neural network-based object detector for vehicle detection and recognition applications in aerial images.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Alexander Pichler and Nicolas Hueber "Automated method for generating datasets of infrared or visible images for context-specific training of deep neural network-based object detectors", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330I (19 October 2023); https://doi.org/10.1117/12.2684062

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