Maritime detection framework 2.0: A new approach of maritime target detection in electro-optical sensors

Maritime detection framework 2.0: A new approach of maritime target detection in electro-optical sensors

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.

Hickman, D.L. (editor)
Bursing, H. (editor)
Huckridge, D.A. (editor)

2018

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.

Automated vessel detection
Electro-optics
IRST
Ship detection
Deep learning
Errors
Infrared devices
Optical data processing
Signal detection
Automated vessel detection
Detection performance
Detection probabilities
Electrooptical sensors
IRST
Ship detection
Technological trends
Tracking technology
Image processing

http://resolver.tudelft.nl/uuid:f03df66d-269c-4cf1-99b1-52b44cde2c41

https://doi.org/10.1117/12.2501424

844196

SPIE

9781510621732

0277-786X

Proceedings of SPIE - The International Society for Optical Engineering, 10795

1079507

The Society of Photo-Optical Instrumentation Engineers

conference paper