Ship detection in harbour surveillance based on large-Scale data and CNNs

This paper aims at developing a real-time vessel detection and tracking system using surveillance cameras in harbours with the purpose to improve the current Vessel Tracking Systems (VTS) performance. To this end, we introduce a novel maritime dataset, containing 70,513 ships in 48,966 images, covering 10 camera viewpoints indicating real-life ship traffic situations. For detection, a Convolutional Neural Network (CNN) detector is trained, based on the Single Shot Detector (SSD) from literature. This detector is modified and enhanced to support the detection of extreme variations of ship sizes and aspect ratios. The modified SSD detector offers a high detection performance, which is based on explicitly exploiting the aspect-ratio characteristics of the dataset. The performance of the original SSD detector trained on generic object detection datasets (including ships) is significantly lower, showing the added value of a novel surveillance dataset for ships. Due to the robust performance of over 90% detection, the system is able to accurately detect all types of vessels. Hence, the system is considered a suitable complement to conventional radar detection, leading to a better operational picture for the harbour authorities.