自动分割珊瑚礁图像，大幅提高标注效率与精度。
CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask
- 基于定制化机器学习模型，实现珊瑚区域的自动密集分割。
- 相比现有工具，标注速度更快，准确率和精确度显著提升。
- 适合生态监测、海洋科研人员快速获取高质量分割结果。
珊瑚礁图像为监测生态系统健康提供关键数据，随着图像数据集迅速扩展，其分析需求日益增长。尽管半自动化分析平台逐渐普及，但主流方法仍存在根本性局限。为此，我们提出CoralSCOP-LAT，一种用于珊瑚礁图像分析与标注的工具，可自动分割并分析珊瑚区域。通过采用专为珊瑚礁分割优化的先进机器学习模型，CoralSCOP-LAT能以极少人工干预生成密集分割掩码，显著提升标注效率与精度。大量评估表明，CoralSCOP-LAT在时间效率、准确性、精确度和灵活性方面均优于现有工具。该工具不仅加速了珊瑚礁标注流程，还帮助用户获得高质量的分割与分析结果。GitHub 页面：https://github.com/ykwongaq/CoralSCOP-LAT。
原文摘要 · Abstract (English)
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.
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