Broken ice circumferential crack estimation via image techniques

The identification and digitization of floating ice are of great significance to developing numerical ice load model of ships and marine structures in managed ice fields. The circumferential crack method is a commonly-used method to simulate the level ice breaking process for a polar ship. The crack size, including radius and open -angle, is an import parameter that should be considered in the analysis of ice-structure interactions. It is usu-ally determined by the empirical formulas, which may introduce many uncertainties in the selection of input parameters. In this paper, we adopt a deep learning YOLACT model to identify broken ice blocks from an image, where an image processing algorithm is newly developed to estimate circumferential crack size for each iden-tified ice blocks. The numerical simulation shows that the accuracy of the radius and open-angle estimated by the present method can reach up to 97.37% and 96.66%, respectively. The present tool could be used to assist design and operations for marine structures in ice-infested waters.

Automated summary

The identification and digitization of floating ice are crucial for developing numerical ice load models for ships and marine structures in managed ice fields. This paper presents a deep learning approach for identifying broken ice blocks from images and estimating their circumferential crack size. The numerical simulation demonstrates a high accuracy in estimating the radius and open-angle of the cracks using this method.