LEMMA: Laplacian pyramids for Efficient Marine SeMAntic Segmentation
2026-03-26 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created LEMMA, a simpler and faster computer model that helps boats and satellites identify different things in the ocean, like oil spills. Unlike older methods that need lots of computer power, LEMMA uses a smart technique called Laplacian Pyramids to spot edges early on, which makes it much lighter and quicker. Their tests show it works well on real marine images, performing almost as accurately as bigger models but using up to 71 times fewer resources. This makes it practical for real-time use in monitoring oceans without needing expensive equipment.
Semantic segmentationUnmanned Surface VesselsLaplacian PyramidsEdge recognitionRemote sensingDeep CNNsTransformersOil spill detectionModel efficiencyInference time
Authors
Ishaan Gakhar, Laven Srivastava, Sankarshanaa Sagaram, Aditya Kasliwal, Ujjwal Verma
Abstract
Semantic segmentation in marine environments is crucial for the autonomous navigation of unmanned surface vessels (USVs) and coastal Earth Observation events such as oil spills. However, existing methods, often relying on deep CNNs and transformer-based architectures, face challenges in deployment due to their high computational costs and resource-intensive nature. These limitations hinder the practicality of real-time, low-cost applications in real-world marine settings. To address this, we propose LEMMA, a lightweight semantic segmentation model designed specifically for accurate remote sensing segmentation under resource constraints. The proposed architecture leverages Laplacian Pyramids to enhance edge recognition, a critical component for effective feature extraction in complex marine environments for disaster response, environmental surveillance, and coastal monitoring. By integrating edge information early in the feature extraction process, LEMMA eliminates the need for computationally expensive feature map computations in deeper network layers, drastically reducing model size, complexity and inference time. LEMMA demonstrates state-of-the-art performance across datasets captured from diverse platforms while reducing trainable parameters and computational requirements by up to 71x, GFLOPs by up to 88.5\%, and inference time by up to 84.65\%, as compared to existing models. Experimental results highlight its effectiveness and real-world applicability, including 93.42\% IoU on the Oil Spill dataset and 98.97\% mIoU on Mastr1325.