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Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.
Ship collision risk is an important aspect of ship navigation safety. A systematic method to assess collision risk by monitoring parameter states continually is necessary and has proven effective. Another important factor in risk assessment is ship size, but the effect of the size of ship pairs has not been considered properly in many previous studies. This research utilises a systematic perspective to study collision risk of near-misses in ship-ship encounters. This fills a secondary research gap where previous risk assessments only investigated near-misses from the perspective of a single vessel. Following this proposed approach, ship pair encounter states can be continually tracked. Ultimately, a method of improved Vessel Collision Risk Operator (VCRO) to merge risk assessments of both ships is proposed through integration of near-miss collision risks in a systematic way, which overcomes the disadvantages of prior VCROs that only consider the maximum value, from which it is difficult to track and judge the risk trend. Utilising a case study, the effectiveness of the proposed method is validated through analysis of ship encounters, with ships of different sizes in the Baltic Sea.
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