Data · dataset · 2024
TreePillars: An end-to-end 3D target recognition algorithm for nursery and orchard spray robot
Listed in Teesside University Research Data Repository
The dataset used in this study includes 1000 frames of point cloud data featuring scenes of osmanthus and cherry blossom trees.
Description
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.
Read the rest (2 more)
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.
Links
Where it is published
- DOI doi.org/10.17632/ymg89yyj4j.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Control engineering, mechatronics and robotics 72%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/ymg89yyj4j.1 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4007 | enrichment · researchdata tees ac uk | taxonomy-embedding@1.0.0 | title+keywords+description (72%) |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |