Detecting the Unexpected via Image Resynthesis

Lis, Krzysztof; Nakka, Krishna Kanth; Fua, Pascal; Salzmann, Mathieu

doi:10.48550/arxiv.1904.07595

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2019

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“…[1] adapted and evaluated these approaches towards novelty detection in semantic segmentation. As a third direction of research, learned representations of the input are reconstructed and compared in input space [7,8].…”

Section: Related Workmentioning

confidence: 99%

Active Learning for UAV-based Semantic Mapping

Blum¹,

Rohrbach²,

Popović³

et al. 2019

Preprint

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Unmanned aerial vehicles combined with computer vision systems, such as convolutional neural networks, offer a flexible and affordable solution for terrain monitoring, mapping, and detection tasks. However, a key challenge remains the collection and annotation of training data for the given sensors, application, and mission. We introduce an informative path planning system that incorporates novelty estimation into its objective function, based on research for uncertainty estimation in deep learning. The system is designed for data collection to reduce both the number of flights and of annotated images. We evaluate the approach on real world terrain mapping data and show significantly smaller collected training dataset compared to standard lawnmower data collection techniques.

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Section: Related Workmentioning

confidence: 99%