LAqua: Laplacian Pyramids for Aquatic Segmentation

Document Type

Conference Proceeding

Publication Title

Proceedings of the Aaai Conference on Artificial Intelligence

Abstract

Semantic segmentation of marine environments is essential for autonomous navigation of unmanned surface vessels (USVs) as well as the detection of environmental hazards such as oil spills. To tackle the challenges of accurate environmental perception, we propose a lightweight semantic segmentation network, LAqua (Laplacians for Aquatic Segmentation), which leverages Laplacian pyramids to enhance edge detection in marine imagery. Our method drastically reduces computational requirements while maintaining high accuracy in generating semantic masks for marine environments. We evaluate LAqua on two distinct datasets: one focused on detecting oil spills in port environments and another on environmental segmentation for USVs. Results show that LAqua not only performs well across varied marine settings but also achieves comparable or superior segmentation accuracy with far fewer parameters than other models. This efficiency highlights LAqua's potential for applications in real-time detection for marine environments.

First Page

29498

Last Page

29500

DOI

10.1609/aaai.v39i28.35302

Publication Date

4-11-2025

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