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                <identifier>oai:data.mendeley.com/ymg89yyj4j.1</identifier>
                <datestamp>2024-12-23T09:56:50Z</datestamp>
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    <dc:creator>Liu, Hui</dc:creator>
    <dc:title>TreePillars: An end-to-end 3D target recognition algorithm for nursery and orchard spray robot</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>The dataset used in this study includes 1000 frames of point cloud data featuring scenes of osmanthus and cherry blossom trees. The data were collected using a Velodyne 16-line LiDAR during three distinct acquisition periods to capture seasonal variations in tree structure: November 2022, May 2023, and April 2024. This approach was intentionally designed to ensure a comprehensive and diverse dataset, with each period reflecting different stages in the trees’ growth cycle. The initial data collection in November 2022, referenced from Liu et al. (2024b,a), provides a baseline to observe changes in tree characteristics over time.
The trained TreePillars algorithm achieves an mAP of 61.34%, a computation complexity of 7.34 GFLOPs, and a parameter count of 4.24 million, respectively. Compared to PointPillars, the average precision is increased by 10.94%, while the computation complexity and parameters count are reduced by 4.6% and 12.4%, respectively. It has a more accurate recognition effect on sparse and occluded point cloud targets. Experiments on the intelligent robot show that the location relative error for trees, mAP, and average time for single-frame detection of TreePillars are 1.17%, 77.44%, and 19.14ms, respectively. Compared to the improved DBSCAN and lightweight PointNet, the location relative error is reduced by 1.88%, with detection results showing precise bounding boxes for targets, and a detection time shortened by 76.77%. Relative to the VoteNet algorithm, TreePillars reduces the positiong error by 0.7%, improves mAP by 26.33%, and shortens detection time by 98.29%, meeting the requirements of real-time target detection.</dc:description>
    <dc:subject>Pedestrian</dc:subject>
    <dc:subject>Tree</dc:subject>
    <dc:contributor>zhuofan, shen</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/ymg89yyj4j.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/ymg89yyj4j.1</dc:identifier>
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    <dc:date>2024-12-23T09:56:50Z</dc:date>
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