Print Email Facebook Twitter Maritime detection framework 2.0: A new approach of maritime target detection in electro-optical sensors Title Maritime detection framework 2.0: A new approach of maritime target detection in electro-optical sensors Author van der Stap, N. van Opbroek, A.G. Huizinga, W. Wilmer, M.M.G. van den Broek, S.P. Pruim, R.H.R. den Hollander, R.J.M. Schutte, K. Dijk, J. Contributor Hickman, D.L. (editor) Bursing, H. (editor) Huckridge, D.A. (editor) Publication year 2018 Abstract Detecting maritime targets with electro-optical sensors is an active area of research. One current trend is to automate target detection through image processing or computer vision. Automation of target detection will decrease the number of people required for lower-level tasks, which frees capacity for higher-level tasks. A second trend is that the targets of interest are changing; more distributed and smaller targets are of increasing interest. Technological trends enable combined detection and identification of targets through machine learning. These trends and new technologies require a new approach in target detection strategies with specific attention to choosing which sensors and platforms to deploy. In our current research, we propose 'maritime detection framework 2.0', in which multi-platform sensors are combined with detection algorithms. In this paper, we present a comparison of detection algorithms for EO sensors within our developed framework and quantify the performance of this framework on representative data. Automatic detection can be performed within the proposed framework in three ways: 1) using existing detectors, such as detectors based on movement or local intensities; 2) using a newly developed detector based on saliency on the scene level; and 3) using a state-of-The-Art deep learning method. After detection, false alarms are suppressed using consecutive tracking approaches. The performance of these detection methods is compared by evaluating the detection probability versus the false alarm rate for realistic multi-sensor data. New types of maritime targets require new target detection strategies. Combining new detection strategies with existing tracking technologies shows potential increase in detection performance of the complete framework. © 2018 SPIE. Subject Automated vessel detectionElectro-opticsIRSTShip detectionDeep learningErrorsInfrared devicesOptical data processingSignal detectionAutomated vessel detectionDetection performanceDetection probabilitiesElectrooptical sensorsIRSTShip detectionTechnological trendsTracking technologyImage processing To reference this document use: http://resolver.tudelft.nl/uuid:f03df66d-269c-4cf1-99b1-52b44cde2c41 DOI https://doi.org/10.1117/12.2501424 TNO identifier 844196 Publisher SPIE ISBN 9781510621732 ISSN 0277-786X Source Proceedings of SPIE - The International Society for Optical Engineering, 10795 Article number 1079507 Bibliographical note The Society of Photo-Optical Instrumentation Engineers Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.