Next Article in Journal
Robust 3D Object Model Reconstruction and Matching for Complex Automated Deburring Operations
Next Article in Special Issue
Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards
Previous Article in Journal
Hyperspectral Unmixing from Incomplete and Noisy Data
Previous Article in Special Issue
Imaging for High-Throughput Phenotyping in Energy Sorghum
Article Menu

Export Article

Open AccessArticle
J. Imaging 2016, 2(1), 6; doi:10.3390/jimaging2010006

Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture

Department of Engineering, Aarhus University, Finlandsgade 22 8200 Aarhus N, Denmark
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Francisco Rovira-Más and Gonzalo Pajares Martinsanz
Received: 18 December 2015 / Revised: 29 January 2016 / Accepted: 2 February 2016 / Published: 15 February 2016
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
View Full-Text   |   Download PDF [13912 KB, uploaded 15 February 2016]   |  

Abstract

In this paper, an algorithm for obstacle detection in agricultural fields is presented. The algorithm is based on an existing deep convolutional neural net, which is fine-tuned for detection of a specific obstacle. In ISO/DIS 18497, which is an emerging standard for safety of highly automated machinery in agriculture, a barrel-shaped obstacle is defined as the obstacle which should be robustly detected to comply with the standard. We show that our fine-tuned deep convolutional net is capable of detecting this obstacle with a precision of 99 . 9 % in row crops and 90 . 8 % in grass mowing, while simultaneously not detecting people and other very distinct obstacles in the image frame. As such, this short note argues that the obstacle defined in the emerging standard is not capable of ensuring safe operations when imaging sensors are part of the safety system. View Full-Text
Keywords: deep learning; obstacle detection; autonomous; ISO deep learning; obstacle detection; autonomous; ISO
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Steen, K.A.; Christiansen, P.; Karstoft, H.; Jørgensen, R.N. Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture. J. Imaging 2016, 2, 6.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top