Robot-supervised Learning of Crop Row Segmentation
| Autor: | Pål Johan From, Jon Glenn Omholt Gjevestad, Richard J. D. Moore, Marianne Bakken, Vignesh Raja Ponnambalam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Image segmentation
Artificial neural network Noise measurement business.industry Computer science Supervised learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Crops Global navigation satellite system Convolutional neural network Field (computer science) Semantics Training Robot RGB color model Segmentation Computer vision Artificial intelligence business Neural networks |
| Zdroj: | ICRA |
| Popis: | We propose an approach for robot-supervised learning that automates label generation for semantic segmentation with Convolutional Neural Networks (CNNs) for crop row detection in a field. Using a training robot equipped with RTK GNSS and RGB camera, we train a neural network that can later be used for pure vision-based navigation. We test our approach on an agri-robot in a strawberry field and successfully train crop row segmentation without any hand-drawn image labels. Our main finding is that the resulting segmentation output of the CNN shows better performance than the noisy labels it was trained on. Finally, we conduct open-loop field trials with our agri-robot and show that row-following based on the segmentation result is accurate enough for closed-loop guidance. We conclude that training with noisy segmentation labels is a promising approach for learning vision-based crop row following. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|