eWaSR -- an embedded-compute-ready maritime obstacle detection network
Matija Ter\v{s}ek, Lojze \v{Z}ust, Matej Kristan

TL;DR
This paper introduces eWaSR, an efficient maritime obstacle detection network optimized for embedded devices, achieving near state-of-the-art accuracy with significantly improved speed and memory efficiency, enabling practical deployment on autonomous surface vehicles.
Contribution
The paper presents eWaSR, a novel lightweight maritime obstacle detection network that maintains high accuracy while being suitable for embedded hardware, based on transformer-based design improvements.
Findings
eWaSR achieves only 0.52% F1 score drop compared to WaSR.
eWaSR runs 10x faster on GPU than WaSR (115 FPS vs 11 FPS).
eWaSR operates at 5.5 FPS on embedded OAK-D device.
Abstract
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…
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Taxonomy
TopicsMaritime Navigation and Safety · Underwater Vehicles and Communication Systems · Advanced Neural Network Applications
