海上ニアミス回避システムのための赤外画像からの船種の判別

抄録

The avoidance of ship collisions becomes an important issue for maritime traffic safety with the increase of traffic density in the inland sea and coastal region.
We have evaluated the risk of ship collisions during meeting through the analyses of radar and IR image information.
This paper describes identification of the category of a target vessel from IR image. Feature parameters describing the size and shape of the vessel are employed for identification of the vessel category on the basis of the three-dimensional graphic models of representative vessels. These parameters evaluated for each category are learned by a multilayered neural network. The outline of a vessel is extracted from IR image and the feature parameters are inputted to the neural network to examine identification of the vessel category. The result reveals that the identification can be done satisfactorily and the present system can effectively be applied to the avoidance of ship collisions.