Abstract: Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profile, ship license background and object occlusion, variations of ship license plate locations and text types. This paper proposes an efficient method based on fully convolutional neural network for ship detection and recognition named SDR-FCN. SDR-FCN, which uses a tiny fully convolutional neural network named SDNet to locate ships, then detects text of plate with PDNet designed in this paper, at last, recognizes the plate with an online adaptive classifier named OA-Classifier. The recognition accuracy of the classifier is improved with integrating the AIS (Automatic Identification System) information. The actual SDR-FCN deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages.

Key words: ship detection, ship license plate recognition, fully convolutional neural network, YOLO, AIS, online adaptive