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J. Imaging 2016, 2(1), 6;

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)
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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

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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.

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