Dynamic Object Removal from Unpaired Images for Agricultural Autonomous Robots

Hiroyasu Akada, Masaki Takahashi

研究成果: Conference contribution


Recently, the demand for agricultural autonomous robots has been increasing. Using the technology of vision-based robotic environmental recognition, they can generally follow farmers to support their work activities, such as conveyance of the harvest. However, a major issue arises in that dynamic objects (including humans) often enter images that the robots rely on for environmental recognition tasks. These dynamic objects degrade the performance of image recognition considerably, resulting in collisions with crops or ridges when the robots are following the worker. To address the occlusion issue, generative adversarial network (GAN) solutions can be adopted as they feature a generative capability to reconstruct the area behind dynamic objects. However, precedented GAN methods basically presuppose paired image datasets to train their networks, which are difficult to prepare. Therefore, a method based on unpaired image datasets is desirable in real-world environments, such as a farm. For this purpose, we propose a new approach by integrating the state-of-the-art neural network architecture, CycleGAN, and Mask R CNN. Our system is trained with a human-tracking dataset collected by an agricultural autonomous robot in a farm. We evaluate the performance of our system both qualitatively and quantitatively for the task of human removal in images.

ホスト出版物のタイトルIntelligent Autonomous Systems 16 - Proceedings of the 16th International Conference IAS-16
編集者Marcelo H. Ang Jr, Hajime Asama, Wei Lin, Shaohui Foong
出版社Springer Science and Business Media Deutschland GmbH
出版ステータスPublished - 2022
イベント16th International Conference on Intelligent Autonomous Systems, IAS-16 2020 - Virtual, Online
継続期間: 2021 6月 222021 6月 25


名前Lecture Notes in Networks and Systems
412 LNNS


Conference16th International Conference on Intelligent Autonomous Systems, IAS-16 2020
CityVirtual, Online

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 信号処理
  • コンピュータ ネットワークおよび通信


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