VaDA简介：面向海事目标分割的新型图像分割模型及配套新数据集
Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Dataset
AI总结：
针对海事环境目标识别受反光、天气等干扰且现有模型、数据集适配自主导航系统不足的问题，提出VaDA分割模型与IFCP实时评估方法，还发布OASIs基准数据集以规范多场景海事模型评估。
摘要：
在计算机视觉人工智能（AI）技术进步的推动下，海运行业正经历快速发展。随之而来的是，借助传感器技术与计算性能的提升，针对海事运输的AI目标识别模型研究正稳步增长。然而，海事环境下的目标识别面临反光、干扰、强光及多种天气条件等挑战。为应对这些挑战，亟需适配海事图像的高性能深度学习算法，以及专门针对海事场景的高质量数据集。现有AI识别模型和数据集在构建自主导航系统方面的适用性有限。因此，本文提出一种用于海事目标分割的垂直与细节注意力（Vertical and Detail Attention, VaDA）模型，以及一种新的模型评估方法——综合计算性能指标（Integrated Figure of Calculation Performance, IFCP），用于实时验证该模型对系统的适用性。此外，我们还推出了一个基准海事数据集OASIs（Ocean AI Segmentation Initiatives，海洋AI分割倡议数据集），以规范不同海事环境下的模型性能评估。OASIs数据集及详细信息可在我们的网站获取：https://www.navlue.com/dataset
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
