Enhanced tree trunk detection for the autonomous field mower via LiDARcamera fusion in complex environments.
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| Title: | Enhanced tree trunk detection for the autonomous field mower via LiDARcamera fusion in complex environments. |
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| Authors: | Ji, Jie1 jijiess@swu.edu.cn, Yang, Jianhang1 yangjianhang2002@163.com, Wang, Mengling1 wangmengling2020@163.com, Zhang, Bohan1 zhangbohan@swu.edu.cn, Liu, Yang2 2432676746@qq.com |
| Source: | International Journal of Agricultural & Biological Engineering. Feb2026, Vol. 19 Issue 1, p213-225. 13p. |
| Subjects: | Multisensor data fusion, Tree trunks, Object recognition (Computer vision) |
| Abstract: | The increasingly widespread application of autonomous field mowers in agriculture has significantly heightened the demand for precise and reliable tree trunk detection technologies, particularly in complex and challenging operational environments. To overcome the inherent limitations of single-sensor systems, such as the sparse point cloud resolution in Light Detection and Ranging (LiDAR), photometric sensitivity in camera-based methods, and persistent occlusion interference, this study proposes a multi-sensor fusion framework that integrates data from multi-line LiDAR and a monocular camera for robust tree trunk detection. First, a spatio-temporal calibration framework was developed to ensure accurate alignment of multi-source data. Subsequently, the PointPillars network was utilized for efficient extraction of 3D point cloud features, while an improved You Only Look Once Version 8 Nano (YOLOv8n) model was integrated to enable precise 2D image feature extraction. Additionally, the Complete Intersection over Union (CIoU) fusion strategy was adopted to enable effective cross-modal bounding box matching. Experimental results demonstrate that the proposed fusion approach achieves average positioning errors of 0.0619 m in the horizontal direction and 0.0583 m in the vertical direction, along with a tree trunk detection accuracy of 93.68%. This method effectively resolves the false detection issues typically encountered with traditional point cloud clustering algorithms in complex environments, while also mitigating performance degradation in vision-based detection under complex texture conditions. The proposed framework presents an innovative approach to environment-aware perception for autonomous mowing operations. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192721087 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced tree trunk detection for the autonomous field mower via LiDARcamera fusion in complex environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ji%2C+Jie%22">Ji, Jie</searchLink><relatesTo>1</relatesTo><i> jijiess@swu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Jianhang%22">Yang, Jianhang</searchLink><relatesTo>1</relatesTo><i> yangjianhang2002@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Mengling%22">Wang, Mengling</searchLink><relatesTo>1</relatesTo><i> wangmengling2020@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Bohan%22">Zhang, Bohan</searchLink><relatesTo>1</relatesTo><i> zhangbohan@swu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yang%22">Liu, Yang</searchLink><relatesTo>2</relatesTo><i> 2432676746@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Agricultural+%26+Biological+Engineering%22">International Journal of Agricultural & Biological Engineering</searchLink>. Feb2026, Vol. 19 Issue 1, p213-225. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+trunks%22">Tree trunks</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The increasingly widespread application of autonomous field mowers in agriculture has significantly heightened the demand for precise and reliable tree trunk detection technologies, particularly in complex and challenging operational environments. To overcome the inherent limitations of single-sensor systems, such as the sparse point cloud resolution in Light Detection and Ranging (LiDAR), photometric sensitivity in camera-based methods, and persistent occlusion interference, this study proposes a multi-sensor fusion framework that integrates data from multi-line LiDAR and a monocular camera for robust tree trunk detection. First, a spatio-temporal calibration framework was developed to ensure accurate alignment of multi-source data. Subsequently, the PointPillars network was utilized for efficient extraction of 3D point cloud features, while an improved You Only Look Once Version 8 Nano (YOLOv8n) model was integrated to enable precise 2D image feature extraction. Additionally, the Complete Intersection over Union (CIoU) fusion strategy was adopted to enable effective cross-modal bounding box matching. Experimental results demonstrate that the proposed fusion approach achieves average positioning errors of 0.0619 m in the horizontal direction and 0.0583 m in the vertical direction, along with a tree trunk detection accuracy of 93.68%. This method effectively resolves the false detection issues typically encountered with traditional point cloud clustering algorithms in complex environments, while also mitigating performance degradation in vision-based detection under complex texture conditions. The proposed framework presents an innovative approach to environment-aware perception for autonomous mowing operations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.25165/j.ijabe.20261901.10196 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 213 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Tree trunks Type: general – SubjectFull: Object recognition (Computer vision) Type: general Titles: – TitleFull: Enhanced tree trunk detection for the autonomous field mower via LiDARcamera fusion in complex environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ji, Jie – PersonEntity: Name: NameFull: Yang, Jianhang – PersonEntity: Name: NameFull: Wang, Mengling – PersonEntity: Name: NameFull: Zhang, Bohan – PersonEntity: Name: NameFull: Liu, Yang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19346344 Numbering: – Type: volume Value: 19 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Agricultural & Biological Engineering Type: main |
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