Small and large scale critical infrastructures detection based on deep learning using high resolution orthogonal images

• URL: http://arxiv.org/abs/2105.11844v1
• Date: Tue, 25 May 2021 11:38:15 GMT
• Title: Small and large scale critical infrastructures detection based on deep learning using high resolution orthogonal images
• Authors: P\'erez-Hern\'andez Francisco, Rodr\'iguez-Ortega Jos\'e, Benhammou Yassir, Herrera Francisco, Tabik Siham
• Abstract summary: This paper presents a smart dataset as well as a resolution-independent critical infrastructure detection system. In particular, guided by the performance of the detection model, we built a dataset organized into two scales, small and large scale. DetDSCI methodology achieves up to 37,53% F1 improvement with respect to the baseline detector.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of critical infrastructures is of high importance in several fields such as security, anomaly detection, land use planning and land use change detection. However, critical infrastructures detection in aerial and satellite images is still a challenge as each one has completely different size and requires different spacial resolution to be identified correctly. Heretofore, there are no special datasets for training critical infrastructures detectors. This paper presents a smart dataset as well as a resolution-independent critical infrastructure detection system. In particular, guided by the performance of the detection model, we built a dataset organized into two scales, small and large scale, and designed a two-stage deep learning detection of different scale critical infrastructures (DetDSCI) methodology in ortho-images. DetDSCI methodology first determines the input image zoom level using a classification model, then analyses the input image with the appropriate scale detection model. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to the baseline detector.

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