Fast and Intelligent Ice Channel Recognition Based on Row Selection

Abstract

The recognition of ice channels plays a crucial role in developing intelligent ship navigation systems in ice-covered waters. Navigating through ice channels with the assistance of icebreakers is a common operation for merchant ships. Maneuvering within such narrow channels presents a significant challenge for the captain’s skills and ship performance. Therefore, it becomes essential to explore methods for enabling ships to navigate through these channels automatically. A key step in achieving this is the accurate recognition and extraction of boundary lines on both sides of the ice channel. An ice channel line recognition method based on the lane line detection algorithm UFAST is implemented. The method is trained and tested on the constructed ice channel dataset, with the test results showing that the average recognition accuracy reaches 84.1% and the recognition speed reaches 138.3 frames per second, meeting the real-time requirement. In order to solve the current lack of authentic ice channel images, ice channel navigation scenes are built based on UE4, and synthetic ice channel images are rendered. The method in this paper is also compared with the traditional non-intelligent Otsu threshold segmentation method and the intelligent instance segmentation method YOLACT for performance analysis. The method in this paper has 9.5% higher ice channel recognition accuracy and 103.7 frames per second higher recognition speed compared with YOLACT. Furthermore, ablation studies are conducted to analyze the relationship between the number of gridding cells in the proposed method and ice channel recognition accuracy.