2024/07/12 by Jin‐Bum Park, Kim, Yongjin, Park, Jinbum +4 · 2 citations
Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Maritime Navigation and Safety #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.09005
openalex publication_date 2024/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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