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    <title>Efficient Architecture on ViCoS Lab</title>
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      <title>eWaSR — An Embedded-Compute-Ready Maritime Obstacle Detection Network</title>
      <link>/publications/tersek2023ewasr/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;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 current 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 a 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 10× faster than the original WaSR (115 FPS vs. 11 FPS). Tests on a real embedded sensor 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.&lt;/p&gt;</description>
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