Auflistung nach Schlagwort "data annotation"
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- KonferenzbeitragActive-learning-driven deep interactive segmentation for cost-effective labeling of crop-weed image data(43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Sikouonmeu, Freddy; Atzmueller, MartinActive learning has shown its reliability in (semi-)supervised machine learning tasks to reduce the labeling cost for large datasets in various areas. However, in the agricultural field, despite past attempts to reduce the labeling cost and the burden on the labeler in acquiring image labels, the load during the acquisition of pixel-level labels for semantic image segmentation tasks remains high. Typically, the respective pixel-level masks are acquired manually by drawing polygons over irregular and complex-shaped object boundaries. In contrast, this paper proposes a method leveraging the power of a click-based deep interactive segmentation model (DISEG) in an active learning approach to harvest high-quality image segmentation labels at a low cost for training a real-time task model by only clicking on the objects’ fore- and background surfaces. Our first experimental results indicate that with an average of 3 clicks per image object and using only 3% of the unlabeled dataset, we can acquire pixel-level labels with good quality at low cost.
- KonferenzbeitragAI-supported data annotation in the context of UAV-based weed detection in sugar beet fields using Deep Neural Networks(42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Jonas Boysen, Jonas; Stein, AnthonyRecent Deep Learning-based Computer Vision methods proved quite successful in various tasks, also involving the classification, detection and segmentation of crop and weed plants with Convolutional Neural Networks (CNNs). Such solutions require a vast amount of labeled data. The annotation is a tedious and time-consuming task, which often constitutes a limiting factor in the Machine Learning process. In this work, an approach for an annotation pipeline for UAV-based images of sugar beet fields of BBCH-scale 12 to 17 is presented. For the creation of pixel-wise annotated data, we utilize a threshold-based method for the creation of a binary plant mask, a row detection based on Hough Transform and a lightweight CNN for the classification of small, cropped images. Our findings demonstrate that an increased image data annotation efficiency can be reached by using an AI approach already at the crucial Machine Learning-process step of training data collection.