Ariza-Sentís Mar, Wang Kaiwen, Cao Zhen, Vélez Sergio, Valente João
Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, Netherlands.
Data Brief. 2024 Apr 16;54:110432. doi: 10.1016/j.dib.2024.110432. eCollection 2024 Jun.
Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges.
目标检测与跟踪为许多任务提供了一种有价值的工具，这些任务大多既耗时又容易出错，包括在田间进行水果计数等。由于水果数量众多，水果计数对人类来说可能是一项具有挑战性的任务，这使其成为一项耗费脑力的工作。因此，通过实施目标检测与跟踪算法来辅助水果计数，利用技术减轻农民的任务是很有意义的。然而，这些算法由于遮挡问题会出现计数不足的情况，这意味着水果被叶子或树枝遮挡，使检测任务变得复杂。因此，从多个视角收集数据集对于提高从最可见视角记录图像和视频的可能性至关重要。此外，最关键的开源数据集不包括某些水果的标签，例如葡萄串。本研究旨在通过考虑使用无人机从多个角度获取的数据来克服水果遮挡挑战，从而揭示包括标签在内的公共数据集在训练葡萄串检测与跟踪算法方面的稀缺性。
