mlsanityData to 7 Oct 2026

Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Dataset

arXiv 2407.09005 ↗First posted 12 Jul 2024cs.CV cs.AI eess.IVcited by 2

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Abstract (arXiv)

The maritime shipping industry is undergoing rapid evolution driven by advancements in computer vision artificial intelligence (AI). Consequently, research on AI-based object recognition models for maritime transportation is steadily growing, leveraging advancements in sensor technology and computing performance. However, object recognition in maritime environments faces challenges such as light reflection, interference, intense lighting, and various weather conditions. To address these challenges, high-performance deep learning algorithms tailored to maritime imagery and high-quality datasets specialized for maritime scenes are essential. Existing AI recognition models and datasets have limited suitability for composing autonomous navigation systems. Therefore, in this paper, we propose a Vertical and Detail Attention (VaDA) model for maritime object segmentation and a new model evaluation method, the Integrated Figure of Calculation Performance (IFCP), to verify its suitability for the system in real-time. Additionally, we introduce a benchmark maritime dataset, OASIs (Ocean AI Segmentation Initiatives) to standardize model performance evaluation across diverse maritime environments. OASIs dataset and details are available at our website: https://www.navlue.com/dataset

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 12 Jul 2024.

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20 of 34 references.

References · 20

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic segmentationcited by 1
  2. 2025Walk the Lines 2: Contour Tracking for Detailed Segmentationcited by 0