eWaSR -- an embedded-compute-ready maritime obstacle detection network
This work addresses the deployment challenge of maritime obstacle detection on embedded devices for autonomous surface vehicles, representing an incremental improvement over existing methods.
The paper tackled the problem of high computational and memory requirements of maritime obstacle detection networks for autonomous surface vehicles, proposing eWaSR which achieves comparable detection results to the state-of-the-art WaSR with only a 0.52% F1 score drop and runs 10x faster on a GPU, enabling practical deployment on embedded devices like OAK-D at 5.5 FPS.
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: https://github.com/tersekmatija/eWaSR.