Unsupervised obstacle detection for weeding robots: a video transformer-based approach

T. Biardeau; A.-S. Capelle-Laize; S. Alwan; D. Helbert

Unsupervised obstacle detection for weeding robots: a video transformer-based approach

In: Precision agriculture '25

Authors:

T. Biardeau

T. Biardeau

theo.biardeau@univ-poitiers.fr

Search for other papers by T. Biardeau in
Current site
Google Scholar
PubMed

A.-S. Capelle-Laize

A.-S. Capelle-Laize

Search for other papers by A.-S. Capelle-Laize in
Current site
Google Scholar
PubMed

S. Alwan

S. Alwan

Search for other papers by S. Alwan in
Current site
Google Scholar
PubMed

, and

D. Helbert

D. Helbert

Search for other papers by D. Helbert in
Current site
Google Scholar
PubMed

Type:: Chapter

Pages:: 939–948

DOI:: https://doi.org/10.1163/9789004725232_123

Access via:

Dar Hadith al Hassania

Download PDF

Automation of tasks such as mechanical weeding has advanced significantly, especially with autonomous rovers that increasingly rely on AI and RTK GNSS for navigation. However, large multiton machines still face safety challenges, especially in detecting obstacles. Deep learning applied to image analysis is a suitable solution to this challenge. Therefore, VMTAD, an unsupervised videobased approach using a video memory transformer for anomaly detection incorporating temporal information has been developed. VMTAD outperforms previous methods and provides improved obstacle detection that enhances safety and overcomes critical limitations in current robotic systems. The code is available at: https://github.com/TheoBiardeau/VMTAD.

Citation Info

Save
Cite
Email this content

Share link with colleague or librarian

You can email a link to this page to a colleague or librarian:
Email this content
or copy the link directly:

https://brill.edhh.ma/edcollchap-oa/book/9789004725232/BP000123.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

Metrics

	All Time	Past 365 days	Past 30 Days
Abstract Views	0	0	0
Full Text Views	179	138	11
PDF Views & Downloads	250	189	11

African Studies	Education	Media Studies
American Studies	History	Middle East and Islamic Studies
Ancient Near East and Egypt	Human Rights and Humanitarian Law	Musicology
Art History	International Law	Philosophy
Asian Studies	International Relations	Religious Studies
Biblical Studies	Jewish Studies	Slavic and Eurasian Studies
Biology	Languages and Linguistics	Social Sciences
Book History and Cartography	Life Sciences	Theology and World Christianity
Classical Studies	Literature and Cultural Studies

Subjects

Authors

Open Access

Product Information

Company

Contact

Unsupervised obstacle detection for weeding robots: a video transformer-based approach

Citation Info

Share link with colleague or librarian

Precision agriculture '25

Table of Contents

Metrics

Metrics