Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture
AbstractIn 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
Scifeed alert for new publicationsNever 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
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.
Steen KA, Christiansen P, Karstoft H, Jørgensen RN. Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture. Journal of Imaging. 2016; 2(1):6.Chicago/Turabian Style
Steen, Kim A.; Christiansen, Peter; Karstoft, Henrik; Jørgensen, Rasmus N. 2016. "Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture." J. Imaging 2, no. 1: 6.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.