Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

• Published in: China ocean engineering Vol. 38; no. 2; pp. 313 - 325
• Main Authors: Dong, Wen-bo, Zhou, Li, Ding, Shi-feng, Wang, Ai-ming, Cai, Jin-yan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Channels; Clustering; Coastal Sciences; Datasets; Engineering; Fluid- and Aerodynamics; Frames per second; Ice; Ice cover; Ice fields; Icebreakers; Identification methods; Image segmentation; Manoeuvrability; Marine & Freshwater Sciences; Merchant ships; Navigation; Numerical and Computational Physics; Oceanography; Offshore Engineering; Regression; Simulation; ice channel; identification; ship navigation; image segmentation; corner point regression
• ISSN: 0890-5487; 2191-8945
• DOI: 10.1007/s13344-024-0026-x

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters, which is important to ensure the safety and economy of navigation. In the Arctic, merchant ships with low ice class often navigate in channels opened up by icebreakers. Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent. The ship may get stuck if steered into ice fields off the channel. Under this circumstance, it is very important to study how to identify the boundary lines of ice channels with a reliable method. In this paper, a two-staged ice channel identification method is developed based on image segmentation and corner point regression. The first stage employs the image segmentation method to extract channel regions. In the second stage, an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region. A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network. The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset. The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33% on the synthetic ice channel dataset and 70.66% on the real ice channel dataset, and the processing speed can reach up to 14.58 frames per second.