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Examining Difficulties in Weed Detection
Linköping University, Department of Electrical Engineering, Computer Vision.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Automatic detection of weeds could be used for more efficient weed control in agriculture. In this master thesis, weed detectors have been trained and examined on data collected by RISE to investigate whether an accurate weed detector could be trained on the collected data. When only using annotations of the weed class Creeping thistle for training and evaluation, a detector achieved a mAP of 0.33. When using four classes of weed, a detector was trained with a mAP of 0.07. The performance was worse than in a previous study also dealing with weed detection. Hypotheses for why the performance was lacking were examined. Experiments indicated that the problem could not fully be explained by the model being underfitted, nor by the object’s backgrounds being too similar to the foreground, nor by the quality of the annotations being too low. The performance was better when training the model with as much data as possible than when only selected segments of the data were used.

Place, publisher, year, edition, pages
2022. , p. 43
National Category
Computer Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-185706OAI: oai:DiVA.org:liu-185706DiVA, id: diva2:1666845
External cooperation
Research Institutes of Sweden
Subject / course
Computer Vision Laboratory
Presentation
2022-05-31, Systemet, Olaus Magnus väg 37, Linköping, 15:00 (English)
Supervisors
Examiners
Available from: 2022-06-14 Created: 2022-06-09 Last updated: 2023-04-03Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf