首个面向荔枝采摘机器人的多品种多阶段图像数据集
An RGB-D Image Dataset for Lychee Detection and Maturity Classification for Robotic Harvesting
- 构建包含RGB与深度图像的多场景荔枝数据集
- 覆盖4个品种3个成熟度，含9658个标注实例
- 适合农业机器人、计算机视觉研究者使用
荔枝是高价值亚热带水果，基于视觉的采摘机器人可显著提升效率并减少人力依赖。然而，目前缺乏在自然生长环境下一致且全面标注的开源荔枝数据集。为此，我们构建了一个用于荔枝检测与成熟度分类的数据集。在不同天气条件、一天中不同时段采集了多个品种（如诺米迟、妃子笑、黑叶、怀枝）的彩色（RGB）图像，并配以深度图像。数据集涵盖三个成熟阶段，共包含11,414张图像：878张原始RGB图像、8,780张增强后的RGB图像和1,756张深度图像。所有图像均标注了9,658对用于检测与成熟度分类的标签。为保证标注一致性，由三人独立标注，再由第四人审核汇总。进行了详细统计分析，并使用三种代表性深度学习模型验证了数据集的有效性。该数据集已公开供学术使用。
原文摘要 · Abstract (English)
Lychee is a high-value subtropical fruit. The adoption of vision-based harvesting robots can significantly improve productivity while reduce reliance on labor. High-quality data are essential for developing such harvesting robots. However, there are currently no consistently and comprehensively annotated open-source lychee datasets featuring fruits in natural growing environments. To address this, we constructed a dataset to facilitate lychee detection and maturity classification. Color (RGB) images were acquired under diverse weather conditions, and at different times of the day, across multiple lychee varieties, such as Nuomici, Feizixiao, Heiye, and Huaizhi. The dataset encompasses three different ripeness stages and contains 11,414 images, consisting of 878 raw RGB images, 8,780 augmented RGB images, and 1,756 depth images. The images are annotated with 9,658 pairs of lables for lychee detection and maturity classification. To improve annotation consistency, three individuals independently labeled the data, and their results were then aggregated and verified by a fourth reviewer. Detailed statistical analyses were done to examine the dataset. Finally, we performed experiments using three representative deep learning models to evaluate the dataset. It is publicly available for academic
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