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

Author： Steen Kim Arild Christiansen Peter Karstoft Henrik Jørgensen Rasmus Nyholm

Publisher： MDPI

E-ISSN： 2313-433x|2|1|6-6

ISSN： 2313-433x

Source： Journal of Imaging, Vol.2, Iss.1, 2016-02, pp. : 6-6

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

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.