Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation

Name: Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation
Creator: Purdue University Research Repository
Published: 2025-12-18 22:14:33
License: 暂无描述

DataCite Commons2025-12-18 更新2024-07-13 收录

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https://purr.purdue.edu/publications/4105/1

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Autonomous navigation of Unmanned Aerial Vehicle (UAV) along and Autonomous Surface Vehicle (ASV) within rivers and creeks has been a popular research area in recent years, where semantic segmentation neural networks have been implemented to recognize the navigable space. Currently, it is still difficult to release the power of deep semantic segmentation learning for ASV to make long-range navigation plans, and to swiftly and safely do obstacle avoidance while navigating in narrow, rapidly flowing and obstacle intensive creeks/rivers due to lack of aerial fluvial scene data for supervised training. To tackle this problem, and to enrich the aerial fluvial semantic segmentation training data, we collected aerial BEV (birds-eye-view) images, with multiple camera perspectives, of fluvial scenes with drone that flew above inland waterways. Images have been manually selected and semantically labeled with emphasis on extruded obstacles from river and riverbank, to form the novel dataset with 8 classes (Water, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone itself and in-river Obstacles) and 816 high-resolution (2K and 2.7K) images in total.

无人飞行器（Unmanned Aerial Vehicle, UAV）与自主水面载具（Autonomous Surface Vehicle, ASV）在河流及小溪中的自主导航，近年来已成为领域内的热门研究方向。现有研究已借助语义分割神经网络实现可航行空间的识别。当前，由于缺乏用于监督训练的航拍河流场景数据，难以充分发挥深度语义分割学习的效能以支撑ASV完成长距离导航规划，且无法在狭窄、急流且障碍物密集的河道中快速安全地完成避障操作。为解决该问题并丰富航拍河流语义分割训练数据集，我们依托无人机在内陆水道上空飞行，采集了多镜头视角的河流场景鸟瞰视角（birds-eye-view, BEV）图像。所有图像均经过人工筛选，并以河道及河岸的凸起障碍物为重点进行语义标注，最终构建了包含8个类别的全新数据集：水面、船只、桥梁、天空、森林植被、干燥沉积物、无人机本体以及河道内障碍物，总计816张分辨率为2K及2.7K的高分辨率图像。

提供机构：

Purdue University Research Repository

创建时间：

2022-07-18

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