농업과학연구 (Korean Journal of Agricultural Science)

  • 제47권2호
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  • Pages.205-217
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  • 2020
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  • 2466-2402(pISSN)
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  • 2466-2410(eISSN)
충남대학교 농업과학연구소 (Institute of Agricultural Science, CNU)
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Tillage boundary detection based on RGB imagery classification for an autonomous tractor

  • Kim, Gookhwan (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Seo, Dasom (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Kim, Kyoung-Chul (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Hong, Youngki (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Lee, Meonghun (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Lee, Siyoung (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Kim, Hyunjong (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Ryu, Hee-Seok (Department of Agricultural Engineering, National Institute of Agricultural Sciences) ;
  • Kim, Yong-Joo (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Chung, Sun-Ok (Department of Biosystems Mechanical Engineering, Chungnam National University) ;
  • Lee, Dae-Hyun (Department of Biosystems Mechanical Engineering, Chungnam National University)
  • 투고 : 2020.01.08
  • 심사 : 2020.02.21
  • 발행 : 2020.06.01
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초록

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

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피인용 문헌

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