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<p>We introduce DBANet, a dual-branch aggregation network designed for efficient and real-time semantic segmentation of omnidirectional images in maritime environments. To support research and evaluation in this area, we also present the maritime omnidirectional semantic segmentation dataset, which fills the gap in maritime omnidirectional image segmentation. While omnidirectional vision systems are increasingly popular for their 360-degree perception capabilities, their large field of view imposes significant computational demands, and comprehensive evaluation methods for semantic segmentation in such scenarios remain limited. Our approach addresses these challenges by providing a robust and computationally efficient solution applicable to intelligent perception for maritime surface vehicles. Experimental results highlight the performance of DBANet, achieving 92.36 mIoU at 4.94 FPS on the MODSS dataset and 85.08 mIoU at 30.25 FPS on the MaSTr1325 dataset, outperforming state-of-the-art models in both accuracy and efficiency.</p>

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Dbanet: a dual branch aggregation network for real-time semantic segmentation of omnidirectional images in maritime environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月20日

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  • Chenming Li,
  • Chengtao Cai,
  • Jinwhan Kim,
  • Wentao Zhou,
  • Renjie Qiao

摘要

We introduce DBANet, a dual-branch aggregation network designed for efficient and real-time semantic segmentation of omnidirectional images in maritime environments. To support research and evaluation in this area, we also present the maritime omnidirectional semantic segmentation dataset, which fills the gap in maritime omnidirectional image segmentation. While omnidirectional vision systems are increasingly popular for their 360-degree perception capabilities, their large field of view imposes significant computational demands, and comprehensive evaluation methods for semantic segmentation in such scenarios remain limited. Our approach addresses these challenges by providing a robust and computationally efficient solution applicable to intelligent perception for maritime surface vehicles. Experimental results highlight the performance of DBANet, achieving 92.36 mIoU at 4.94 FPS on the MODSS dataset and 85.08 mIoU at 30.25 FPS on the MaSTr1325 dataset, outperforming state-of-the-art models in both accuracy and efficiency.