Virtual multi-modal object detection and classification with deep convolutional neural networks

Nikolaos Mitsakos; Manos Papadakis

doi:10.1117/12.2529233

9 September 2019 Virtual multi-modal object detection and classification with deep convolutional neural networks

Nikolaos Mitsakos, Manos Papadakis

Proceedings Volume 11138, Wavelets and Sparsity XVIII; 1113805 (2019) https://doi.org/10.1117/12.2529233
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

In this paper we demonstrate how the post-processing of gray-scale images with algorithms which have a singularity enhancement effect can assume the role of auxiliary modalities, as in the case where an intelligent system fuses information from multiple physical modalities. We show that as in multimodal AI-fusion, “virtual” multimodal inputs can improve the performance of object detection. We design, implement and test a novel Convolutional Neural Network architecture, based on the Faster R-CNN network for multiclass object detection and classification. Our architecture combines deep feature representations of the input images, generated by networks trained independently on physical and virtual imaging modalities. Using an analog of the ROC curve, the Average Recall over Precision curve, we show that the fusion of certain virtual modality inputs, capable of enhancing singularities and neutralizing illumination, improve recognition accuracy.

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Nikolaos Mitsakos and Manos Papadakis "Virtual multi-modal object detection and classification with deep convolutional neural networks", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 1113805 (9 September 2019); https://doi.org/10.1117/12.2529233

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KEYWORDS

Sensors

Machine learning

Target detection

Data modeling

Image enhancement

Network architectures

Performance modeling

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