A Real-Time Scene Parsing Network for Autonomous Maritime Transportation

On the sea, scene parsing, one of the most basic environmental perception methods for autonomous tasks, helps autonomous devices safely navigate and avoid obstacles. However, most of the existing methods lack real-time performance and have difficulty accurately detecting small obstacles and changeable textures. We, therefore, propose a real-time scene-parsing network for autonomous maritime transportation. Specifically, we first design a lightweight model framework and then explore three efficient loss functions, such that a balance between accuracy and real-time performance can be achieved. The first loss function is an obstacle-weighted loss, which improves the extraction of small obstacles by analyzing the distribution law of obstacles at sea. The second one is a detail loss, which optimizes the detail segmentation at complex contours by emphasizing edge features. The last one is an affinity loss, utilizing the context dependency between features to accurately detect reflections, ripples, and other changeable textures. In addition, a new maritime sense-parsing dataset called Greater Bay Area (GBA) dataset is proposed and made publicly available. We tested the proposed model on the GBA dataset and MaSTr1325 dataset, and the experimental results show that the proposed method achieves superior performance in both segmentation and speed, with mean intersection over union (mIOU) of 94.59% and an FPS of 39.44.