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

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.