Maritime obstacle detection is critical for safe navigation of autonomous
surface vehicles (ASVs). While the accuracy of image-based detection methods
has advanced substantially, their computational and memory requirements
prohibit deployment on embedded devices. In this paper we analyze the currently
best-performing maritime obstacle detection network WaSR. Based on the analysis
we then propose replacements for the most computationally intensive stages and
propose its embedded-compute-ready variant eWaSR. In particular, the new design
follows the most recent advancements of transformer-based lightweight networks.
eWaSR achieves comparable detection results to state-of-the-art WaSR with only
0.52% F1 score performance drop and outperforms other state-of-the-art
embedded-ready architectures by over 9.74% in F1 score. On a standard GPU,
eWaSR runs 10x faster than the original WaSR (115 FPS vs 11 FPS). Tests on a
real embedded device OAK-D show that, while WaSR cannot run due to memory
restrictions, eWaSR runs comfortably at 5.5 FPS. This makes eWaSR the first
practical embedded-compute-ready maritime obstacle detection network. The
source code and trained eWaSR models are publicly available here:
this https URL

本文提出了一种嵌入式端可用的 eWaSR 网络，采用最新的基于转换器的轻量级网络，相比于最优性能 WaSR 的 F1 得分下降仅为 0.52％，并且胜过其他最先进的嵌入式网络，使得该网络是第一个实用的嵌入式海上障碍物检测网络。