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Related Experiment Video

Updated: Jul 8, 2026

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Published on: October 31, 2011

A New Algorithm for Visual Navigation in Unmanned Aerial Vehicle Water Surface Inspection.

Jianfeng Han^1,2, Xiongwei Gao^1,2, Lili Song^1,2

¹School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This study presents WaterSegLite, an efficient algorithm for Unmanned Aerial Vehicle (UAV) visual navigation during water surface inspections. It enables real-time water body segmentation, improving aquatic environment monitoring.

Keywords:

UAV attention mechanism river tracking semantic segmentation visual navigation

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Area of Science:

Environmental Science
Robotics
Computer Vision

Background:

Water surface inspection is vital for aquatic environmental protection.
Unmanned Aerial Vehicles (UAVs) offer efficient, mobile solutions for large-area water inspections.

Purpose of the Study:

To introduce novel UAV inspection methodologies for rivers and lakes.
To develop an efficient semantic segmentation algorithm, WaterSegLite, for UAV visual navigation.
To enable real-time water body segmentation and provide positional data for UAVs.

Main Methods:

Developed two UAV inspection methodologies tailored for river and lake environments.
Designed WaterSegLite, a lightweight semantic segmentation algorithm utilizing an aerial perspective and streamlined neural network.
Integrated WaterSegLite for real-time segmentation and UAV visual navigation.

Main Results:

WaterSegLite achieved 93.81% segmentation accuracy (mIoU) and 95.44% F1 score.
Performance surpassed the baseline model by 2.7% (mIoU) and 2.23% (F1 score).
Algorithm reached a processing frame rate of 28.27 fps on airborne devices, meeting real-time requirements.

Conclusions:

WaterSegLite effectively supports real-time water surface inspection by UAVs.
The algorithm provides technical assistance for UAV inspection techniques in aquatic environments.
Presents innovative concepts for advancing intelligent water surface inspection systems.