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RFD-BiSeNet V2: A Lightweight Floodwater Segmentation Network for Vision-Based Environmental Sensing.

Xinyan Li¹, Yining Shi¹, Sijie Wang¹

¹College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Xianyang 712100, China.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces RFD-BiSeNet V2, a novel AI model for accurate floodwater identification using vision-based systems. The lightweight network enhances environmental sensing by improving segmentation in challenging conditions.

Keywords:

BiSeNet V2 flood disaster lightweight semantic segmentation

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Area of Science:

Environmental monitoring
Computer vision
Artificial intelligence

Background:

Flood disasters necessitate reliable environmental sensing for rapid identification.
Vision-based platforms like unmanned surface vehicles (USVs) are effective but face challenges in accurate floodwater segmentation due to complex boundaries and reflections.

Purpose of the Study:

To develop a lightweight semantic segmentation network, RFD-BiSeNet V2, for improved floodwater identification.
To address challenges in vision-based floodwater segmentation, including dynamic contours, reflections, and scale variations.

Main Methods:

Proposed RFD-BiSeNet V2, a lightweight semantic segmentation network building upon BiSeNet V2.
Integrated edge-aware learning, feature refinement, and multi-scale feature fusion modules.
Evaluated on a comprehensive dataset including USV data, UAV imagery, and diverse real-world scenes.

Main Results:

RFD-BiSeNet V2 achieved a mean Intersection over Union (mIoU) of 97.10%, outperforming the baseline by 6.68%.
Edge-aware and feature refinement modules effectively sharpened water boundaries and filtered reflections.
Demonstrated real-time inference capabilities with a compact size of 5.95M parameters.

Conclusions:

RFD-BiSeNet V2 offers a robust and efficient solution for floodwater segmentation in intelligent environmental sensing systems.
The model's architectural advancements provide practical implications for resource-constrained deployments.
The proposed network significantly enhances the accuracy and reliability of vision-based flood monitoring.