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A New Algorithm for Visual Navigation in Unmanned Aerial Vehicle Water Surface Inspection
Jianfeng Han1,2, Xiongwei Gao1,2, Lili Song1,2
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.
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.
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