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

TL;DR

This paper presents VaDA, a new deep learning model for maritime object segmentation, along with a novel dataset and evaluation method to improve autonomous navigation in challenging maritime conditions.

Contribution

The paper introduces VaDA, a specialized segmentation model, and a new maritime dataset, OASIs, along with the IFCP evaluation method, advancing AI-based maritime object recognition.

Findings

VaDA outperforms existing models in maritime segmentation tasks.

OASIs dataset provides diverse maritime environment data.

IFCP offers a standardized performance evaluation.

Abstract

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