The progress in maritime obstacle detection is hindered by the lack of a
diverse dataset that adequately captures the complexity of general maritime
environments. We present the first maritime panoptic obstacle detection
benchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our major
contribution is the new dataset, which boasts the largest diversity in
recording locations, scene types, obstacle classes, and acquisition conditions
among the related datasets. LaRS is composed of over 4000 per-pixel labeled key
frames with nine preceding frames to allow utilization of the temporal texture,
amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuff
classes and 19 global scene attributes. We report the results of 27 semantic
and panoptic segmentation methods, along with several performance insights and
future research directions. To enable objective evaluation, we have implemented
an online evaluation server. The LaRS dataset, evaluation toolkit and benchmark
are publicly available at: this https URL

通过新的数据集 LaRS，我们提供了第一个海洋全景障碍物检测基准，涵盖了湖泊、河流和海洋场景，并提供了最大的多样性录制位置、场景类型、障碍物类别和获取条件。LaRS 数据集由包含 4000 多个像素标记关键帧组成，具有九个先前帧，以利用时间纹理，共计超过 40k 帧。我们报告了 27 种语义和全景分割方法的结果，以及几个性能洞察和未来研究方向。为了实现客观评估，我们还实施了在线评估服务器。LaRS 数据集、评估工具包和基准公开可用。

LaRS：多样性全景海上障碍物检测数据集与基准

LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and  Benchmark

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。