CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask
This addresses the need for more efficient and accurate coral reef monitoring tools for researchers and conservationists, but it appears incremental as it builds on existing semi-automated platforms.
The paper tackles the problem of analyzing coral reef images by proposing CoralSCOP-LAT, a tool that automatically segments and analyzes coral regions, resulting in enhanced labeling efficiency and precision compared to existing tools.
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.