CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images

Tomoya Goto, Genya Ishigami

研究成果: Article査読

抄録

Unmanned mobile robots in rough terrains are a key technology for achieving smart agriculture and smart construction. The mobility performance of robots highly depends on the moisture content of soil, and past few studies have focused on terrain classification using moisture content. In this study, we demonstrate a convolutional neural network-based terrain classifi¬cation method using RGB-infrared (IR) images. The method first classifies soil types and then categorizes the moisture content of the terrain. A three-step image preprocessing for RGB-IR images is also integrated into the method that is applicable to an actual envi-ronment. An experimental study of the terrain classi¬fication confirmed that the proposed method achieved an accuracy of more than 99% in classifying the soil type. Furthermore, the classification accuracy of the moisture content was approximately 69% for pumice and 100% for dark soil. The proposed method can be useful for different scenarios, such as small-scale agri¬culture with mobile robots, smart agriculture for mon¬itoring the moisture content, and earthworks in small areas.

本文言語English
ページ(範囲)1294-1302
ページ数9
ジャーナルJournal of Robotics and Mechatronics
33
6
DOI
出版ステータスPublished - 2021 12月

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 電子工学および電気工学

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