首页|University of Zagreb Researcher Focuses on Robotics (Generating a Dataset for Semantic Segmentation of Vine Trunks in Vineyards Using Semi-Supervised Learning and Object Detection)
University of Zagreb Researcher Focuses on Robotics (Generating a Dataset for Semantic Segmentation of Vine Trunks in Vineyards Using Semi-Supervised Learning and Object Detection)
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A new study on robotics is now available. According to news originating from Zagreb, Croatia, by NewsRx correspondents, research stated, “This article describes an experimentally tested approach using semi-supervised learning for generating new datasets for semantic segmentation of vine trunks with very little human-annotated data, resulting in significant savings in time and resources. The creation of such datasets is a crucial step towards the development of autonomous robots for vineyard maintenance.” Funders for this research include Project Titled Heterogeneous Autonomous Robotic System in Viticulture And Mariculture; European Union Through The European Regional Development Fund-the Competitiveness And Cohesion Operational Programme. The news correspondents obtained a quote from the research from University of Zagreb: “In order for a mobile robot platform to perform a vineyard maintenance task, such as suckering, a semantically segmented view of the vine trunks is required. The robot must recognize the shape and position of the vine trunks and adapt its movements and actions accordingly. Starting with vine trunk recognition and ending with semi-supervised training for semantic segmentation, we have shown that the need for human annotation, which is usually a time-consuming and expensive process, can be significantly reduced if a dataset for object (vine trunk) detection is available. In this study, we generated about 35,000 images with semantic segmentation of vine trunks using only 300 images annotated by a human. This method eliminates about 99% of the time that would be required to manually annotate the entire dataset. Based on the evaluated dataset, we compared different semantic segmentation model architectures to determine the most suitable one for applications with mobile robots.”
University of ZagrebZagrebCroatiaEuropeEmerging TechnologiesMachine LearningNano-robotRobotRoboticsSupervised Learning
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