Abstract
This paper presents a method to detect boats in a maritime surveillance scenario using a small aircraft. This method relies on Convolutional Neural Networks (CNNs) to perform robust detections even in the presence of distractors like wave crests and sun glare. The CNNs are pre-trained on large scale public datasets and then fine-tuned with domain specific images acquired in the maritime surveillance scenario. We study two variations of the method, with one being faster and the other one being more robust. The network’s training procedure is described and the detection performance is evaluated in two different video sequences from UAV flights over the Atlantic ocean. The results are presented as precision-recall curves and computation time and are compared. We show experimentally that, as in many other domains of application, CNNs outperforms non-deep learning methods also in maritime surveillance scenarios.
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Notes
- 1.
The scale variability of the observed ships due to altitude variations is rather small.
- 2.
The authors of each architecture provide the weights, resulting from training on the mentioned dataset, at https://github.com/BVLC/caffe/tree/master/models/.
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Acknowledgements
This work was partially supported by FCT project [UID/EEA/50009/2013]. The authors would like to thank the SEAGULL team and Computer Vision Lab team (VisLab) at ISR/IST, which were involved in obtaining the image dataset and annotating the ground truth.
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Cruz, G., Bernardino, A. (2016). Aerial Detection in Maritime Scenarios Using Convolutional Neural Networks. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_33
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