Vehicle speed and tracking error of the interval type-2 fuzzy controller in the presence of disturbances. حسب Seung-Min Ryu (21463891)

Typical error cases. حسب Wenhao Ren (2561731)

Data Sheet 1_Research on persimmon fruit diameter accurate detection method based on improved RCNN instance segmentation algorithm.zip حسب Yuan Fang (132924)

"…The improved instance segmentation algorithm enhances the segmentation accuracy of the target fruit and reduces the relative error in the fruit diameter measurement caused by sticking problems during occlusion and overlap. …"

Image 7_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 2_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Table 1_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.docx حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 4_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 6_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 5_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 1_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

Image 3_Segmentation of tobacco shred point cloud and 3-D measurement based on improved PointNet++ network with DTC algorithm.png حسب Yihang Wang (4731429)

"…</p>Results<p>The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%.…"

A dataset that includes colony segments, labeled using historical photographs, is used to optimize and evaluate the performance of MCount. حسب Sijie Chen (1622476)

الموضوعات:

Stability analysis of phenotypic parameter measurements 10-fold cross-validation results. حسب Qi Yikai (22298207)

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presents a graphical representation of the manual measurements of the height, stem length, and stem diameter of <i>Schima Superba</i> seedlings. حسب Qi Yikai (22298207)

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Comparison of three different skeletonization methods. حسب Qi Yikai (22298207)

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Preprocessing stages of the point cloud: (a) Real image; (b) Depth image; (c) Raw point cloud; (d) Background-removed point cloud; (e) Flowerpot-removed point cloud; (f) 0-degree a... حسب Qi Yikai (22298207)

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Comparison of four phenotypic parameters: manual measurements and systematic estimates. حسب Qi Yikai (22298207)

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Comparison between the geometric centroid and skeletonization methods employed in this study. حسب Qi Yikai (22298207)

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Illustration of the process for extracting stem skeleton points. حسب Qi Yikai (22298207)

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An example of a typical sample split involving 12 plants, where different colors represent distinct leaves after segmentation. حسب Qi Yikai (22298207)

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