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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >TOMATOD: EVALUATION OF OBJECT DETECTION ALGORITHMS ON A NEW REAL-WORLD TOMATO DATASET
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TOMATOD: EVALUATION OF OBJECT DETECTION ALGORITHMS ON A NEW REAL-WORLD TOMATO DATASET

机译：Tomatod：新真实世界番茄数据集对象检测算法评估

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The integration of modern technologies in farming poses a challenging task to the research community. In this work, the task of selective cropping and treating is considered, whereas learning algorithms can provide essential assistance on crop growth and disease prediction, species recognition and fruit detection. In this paper, we introduce a highly specialized object detection (OD) and classification dataset of tomato fruits that contains class information for the ripening stage of each tomato fruit apart from its corresponding bounding box. With this dataset we aim to encourage the development of task-specific production ready object detection algorithms, as well as to evaluate and provide a baseline result of common state-of-the-art generic OD algorithms. In detail, a thorough presentation of the most common OD datasets takes place, where we discuss both generic OD and some highly specialized datasets. Our dataset contains more than 250 images and 2400 annotations in total. The dataset contains class information for three ripening stages of a tomato fruit provided by expert agriculturists, while providing views consistent with the targeted real-world use case scenario. Compared to other OD datasets our proposition differs in core areas such as the quality of the annotations, the object size distribution and the public availability. Evaluating the performance in our dataset for six object detection models we draw conclusions about the strength and weaknesses of each one’s performance. Finally, we present a future roadmap of revisions and discuss some future research topics that could improve the performance of OD algorithms in our dataset.
机译：现代技术在农业中的整合构成了对研究界的一项挑战任务。在这项工作中，考虑了选择性种植和治疗的任务，而学习算法可以为作物生长和疾病预测，物种识别和水果检测提供基本辅助。在本文中，我们介绍了番茄水果的高度专业的对象检测（OD）和分类数据集，其中包含每个番茄果实的成熟阶段的类信息，除了其相应的边界框。使用此数据集，我们的目标是鼓励开发任务特定的生产就绪对象检测算法，以及评估和提供常见的通用OD算法的基线结果。详细地说明，彻底呈现最常见的OD数据集，我们讨论了一般OD和一些高度专业化的数据集。我们的数据集包含超过250个图像和2400个注释。 DataSet包含专家农业学家提供的番茄果实三个成熟阶段的类信息，同时提供与目标现实世界使用案例方案一致的意见。与其他OD数据集相比，我们的命题在核心区域的不同之处不同，例如注释质量，对象大小分布和公共可用性。评估我们数据集的性能，以获得六个对象检测模型，我们得出关于每个人性能的强度和弱点的结论。最后，我们提出了未来的修订路线图，并讨论了一些未来的研究主题，可以提高我们数据集中的OD算法的性能。

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