DSBEV: Docking Space Segmentation for Autonomous Surface Vehicle in Bird's Eye View

Engineering notes

The experimental results demonstrate that our DSBEV network achieved an average IoU of 76.8%, attaining state-of-the-art performance.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous surface vehicles (ASVs) are widely employed in various marine industrial tasks. Regardless of the specific operation, one common process is docking, particularly the ASV's return to port. Therefore, the docking process is crucial to ASV operations in industrial applications. Docking space perception (DSP) provides essential environmental information, such as the docking space, driving space, and the shore. However, only a few studies have utilized marks for cooperative docking space perception, and no research has explored noncooperative perception. This article proposes a novel docking space bird's eye view (DSBEV) network to segment docking space on the bird's eye view (BEV) plane for the DSP task. DSBEV does not require any marks and can operate directly in port environments. DSBEV network includes an undistortion-splat module and an attention-water module, designed to enhance the DSP performance. A new watershore loss is introduced to generate the docking space utilizing the shape information. Due to the lack of the docking perception dataset, we construct a water surface platform to create a docking space dataset. The experimental results demonstrate that our DSBEV network achieved an average IoU of 76.8%, attaining state-of-the-art performance. Module analyses and ablation studies are conducted to validate the effectiveness of our designed modules.

Links and sources

• Official / arXiv page

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