Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture
By Kim Arild Steen, Peter Hviid Christiansen, Henrik Karstoft, Rasmus Nyholm Jørgensen. 2016
61 citations recorded by the source corpus.
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.
Subfields: Automotive Engineering, Plant Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
- Francesc X. Prenafeta-Boldu
- Andreas Kamilaris
- Ala Al-Fuqaha
- Mohsen Guizani
- Sameh Sorour
- Srdjan Sladojevic
- Andras Anderla
- Marko Arsenovic
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “example, [138] has used a DL model based on deep CNNs for obstacle detection in agricultural fields, which enables autonomous machines to operate safely in them.”
- Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection (2019, background): “Convolutional neural networks (CNNs) have performed well in crop classification tasks [5], fruit counting, yield prediction [6], disease detection [7–9], and vision tasks in general [9,10].”
- A review of the use of convolutional neural networks in agriculture (2018, background): “The majority of these papers dealt with image classification and identification of areas of interest, including detection of obstacles (Christiansen et al., 2016; Steen et al., 2016) and fruit counting (Sa et al.”