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Deep learning algorithms have been proven to be a powerful tool in image and video processing for security and surveillance operations. In a maritime environment, the fusion of electro-optical sensor data with human intelligence plays an important role to counter the security issues. For instance, the situational awareness can be enhanced through an automated system that generates reports on ship identity and signature together with detecting the changes on naval vessels activity. As a result, this improves data gathering and analysis in the absence of sensor specialists on board and significantly increases the response time to anomalous events.
To date, various studies have been set out to explore the performance of deep neural networks using a ship signature database. Research on image analysis in the maritime domain mainly focuses on an object detection task. It follows that ship detection is very challenging due to the illumination and weather conditions, the water dynamics, the complex backgrounds, the presence of small-sized objects and the limited availability of training data. Aside from detection, image segmentation gains interest for maritime surveillance. The task is proposed to address not only the naval vessel detection using bounding boxes, but also obtaining the ship mask. By performing the segmentation to the pixel level the ship characteristics can be more accurately obtained in order to acquire object classification and identification.
In the current study, we investigate the Mask R-CNN method, i.e. a state-of-the-art framework in image segmentation tasks, for ship detection. The surveillance data captured by an on-board camera provides visual-optical videos in an open sea scenario with a minimum influence from background clutter. The results indicate that the detector performs well on large object targets, however, training on a dataset representative of what is expected to detect and recognize is needed.
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