论文标题
LDD：用于葡萄疾病对象检测和实例分段的数据集
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation
论文作者
论文摘要
实例分割任务是众所周知的对象检测任务的扩展，在许多领域（例如精确农业：能够自动识别植物器官以及与之相关的可能疾病）具有很大的帮助，可以有效地扩展和自动化作物监测和自动化作物监测及其疾病控制。为了解决与早期疾病检测和葡萄藤植物诊断有关的问题，已经创建了一个新的数据集，目的是通过实例细分方法来推进疾病识别的最新识别。这是通过收集在自然背景下受疾病影响的叶片和葡萄簇的图像来实现的。该数据集包含10种对象类型的照片，其中包括叶子和葡萄，其中有8种常见的葡萄疾病的症状，其中1,092张图像中总共有17,706个标记实例。提出了多种统计措施，以便对数据集的特征进行完整的看法。蒙版R-CNN和R^3-CNN达到的对象检测和实例分割任务的初步结果作为基线提供，表明该过程能够就自动疾病症状识别的目的达到有希望的结果。
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control. To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the state-of-the-art of diseases recognition via instance segmentation approaches. This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases' symptoms recognition.
