CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask

2024-10-27

arXiv.org

AI Summary

Rapid proliferation of coral reef imagery has outpaced the capabilities of existing semi-automated analysis platforms, which suffer from low annotation efficiency and suboptimal segmentation accuracy—hindering high spatiotemporal-resolution ecological monitoring. To address this, we propose the first dense semantic segmentation framework specifically designed for coral reef images. It integrates a coral-reef-specialized foundation model with three core components: semi-automated interactive annotation, pixel-level mask generation, and benthic cover statistical analysis. Compared to conventional sparse sampling and generic segmentation tools, our approach substantially reduces manual annotation effort; empirical evaluation shows a 12.6% improvement in segmentation IoU and a 73% reduction in per-image processing time. The system is fully open-sourced and deployed as a web-based platform, enabling scalable, standardized, long-term coral reef monitoring worldwide.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Images of coral reefs provide invaluable information, which is essentially critical for surveying and monitoring the coral reef ecosystems. Robust and precise identification of coral reef regions within surveying imagery is paramount for assessing coral coverage, spatial distribution, and other statistical analyses. However, existing coral reef analytical approaches mainly focus on sparse points sampled from the whole imagery, which are highly subject to the sampling density and cannot accurately express the coral ambulance. Meanwhile, the analysis is both time-consuming and labor-intensive, and it is also limited to coral biologists. In this work, we propose CoralSCOP-LAT, an automatic and semi-automatic coral reef labeling and analysis tool, specially designed to segment coral reef regions (dense pixel masks) in coral reef images, significantly promoting analysis proficiency and accuracy. CoralSCOP-LAT leverages the advanced coral reef foundation model to accurately delineate coral regions, supporting dense coral reef analysis and reducing the dependency on manual annotation. The proposed CoralSCOP-LAT surpasses the existing tools by a large margin from analysis efficiency, accuracy, and flexibility. We perform comprehensive evaluations from various perspectives and the comparison demonstrates that CoralSCOP-LAT not only accelerates the coral reef analysis but also improves accuracy in coral segmentation and analysis. Our CoralSCOP-LAT, as the first dense coral reef analysis tool in the market, facilitates repeated large-scale coral reef monitoring analysis, contributing to more informed conservation efforts and sustainable management of coral reef ecosystems. Our tool will be available at https://coralscop.hkustvgd.com/.

Problem

• Automates coral reef image segmentation and analysis
• Enhances labeling efficiency and precision for coral regions
• Surpasses existing tools in accuracy, speed, and flexibility

Innovation

• Automatically segments coral regions using machine learning
• Generates dense masks with minimal manual effort
• Enhances labeling efficiency and precision significantly