Introduction  At present in China, the ships near offshore wind power platforms are mainly monitored by means of the ship AIS system and remote cameras. Such means lacking information technology often require a lot of manpower and material resources. In order to effectively warn the ships near the offshore wind power platform, this paper analyzes the urgent problems to be solved that are encountered in the current offshore ship identification, and proposes an offshore ship fusion recognition algorithm that combines the improved Faster-RCNN network and ship AIS system.

  Method  Firstly, improvement suggestions were proposed for three aspects of the Fast-RCNN model, and the structures such as the backbone network and the loss function were adjusted. Secondly, the ships in the pictures taken by the remote cameras were detected by the improved Faster-RCNN network, and the results were supplemented and corrected in combination with the relevant information from the ship AIS system. Finally, the verification sets were tested according to the optimal model saved in the model training process, and each model was evaluated using the indicators of precision, recall and average precision.

  Result  The Faster-RCNN model inference speed and accuracy for different feature extraction networks and classification loss functions are improved greatly. The ability of offshore wind power platforms to monitor ships is improved. The offshore ship information was processed and the navigation trajectory was obtained in combination with the ship AIS system, realizing the detection of the ships in the pictures taken by the remote cameras.

  Conclusion  Experiments show that the feature extraction network and the replacement of the classification loss function of the traditional Faster-RCNN can effectively improve the detection accuracy of the network in the ship recognition task and the ship trajectory can be effectively obtained by integrating the ship AIS system.