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

TL;DR

CoralSCOP-LAT is a machine learning-based tool that automates dense segmentation and analysis of coral reef images, significantly improving efficiency and accuracy over existing methods.

Contribution

This paper introduces CoralSCOP-LAT, a novel coral reef image analysis tool that automates segmentation with minimal manual effort, outperforming existing solutions.

Findings

Outperforms existing tools in accuracy and efficiency

Reduces manual labeling effort significantly

Provides high-quality coral segmentation results

Abstract

Coral reef imagery offers critical data for monitoring ecosystem health, in particular as the ease of image datasets continues to rapidly expand. Whilst semi-automated analytical platforms for reef imagery are becoming more available, the dominant approaches face fundamental limitations. To address these challenges, we propose CoralSCOP-LAT, a coral reef image analysis and labeling tool that automatically segments and analyzes coral regions. By leveraging advanced machine learning models tailored for coral reef segmentation, CoralSCOP-LAT enables users to generate dense segmentation masks with minimal manual effort, significantly enhancing both the labeling efficiency and precision of coral reef analysis. Our extensive evaluations demonstrate that CoralSCOP-LAT surpasses existing coral reef analysis tools in terms of time efficiency, accuracy, precision, and flexibility. CoralSCOP-LAT, therefore, not only accelerates the coral reef annotation process but also assists users in obtaining high-quality coral reef segmentation and analysis outcomes. Github Page: https://github.com/ykwongaq/CoralSCOP-LAT.