Image based ice-field characterization and load prediction in managed ice field

Accurate modelling of ice properties and ice-structure interaction forces is important for operations of ships and offshore platforms in ice-infested water. Extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, accurate extraction of ice floe information is challenging due to several inherent complexities in ice images. This paper presents an ice image processing technique which can extract useful ice properties from a closely connected, unevenly illuminated floe field (with various floe sizes and shapes) with higher precision, compared to similar existing models. Several image processing features, including histogram equalization, wavelet denoising, gradient flow vector, snake algorithm, and distance transformation were applied for extracting ice features. The effectiveness of the proposed method is demonstrated through the processing of simulated and managed ice field images from ice tank, and its performance is compared with two other existing models. The new model detected the total number of floes with more than 80 % accuracy and ice concentration at 95 % and above accuracy for ice basin test images. It is also nearly 50 % faster compared to the previous model. The extracted ice features' information is then used to train and test two separate force predictors based on Support Vector Machine (SVM) and Feedforward Neural Network (FFNN). This work is a first step towards developing an image-based force prediction tool from real-life ice field.