Detection and Localization of Features on a Soccer Field with Feedforward Fully Convolutional Neural Networks (FCNN) for the Adult-Size Humanoid Robot Sweaty
- For the RoboCup Soccer AdultSize League the
humanoid robot Sweaty uses a single fully convolutional neural
network to detect and localize the ball, opponents and other
features on the field of play. This neural network can be trained
from scratch in a few hours and is able to perform in real-time
within the constraints of computational resources available on the
robot. The time it takes toFor the RoboCup Soccer AdultSize League the
humanoid robot Sweaty uses a single fully convolutional neural
network to detect and localize the ball, opponents and other
features on the field of play. This neural network can be trained
from scratch in a few hours and is able to perform in real-time
within the constraints of computational resources available on the
robot. The time it takes to precess an image is approximately
11 ms. Balls and goal posts are recalled in 99 % of all cases
(94.5 % for all objects) accompanied by a false detection rate
of 1.2 % (5.2 % for all). The object detection and localization
helped Sweaty to become finalist for the RoboCup 2017 in
Nagoya.…
| Author: | Fabian Schnekenburger, Manuel Scharffenberg, Michael WülkerORCiDGND, Ulrich HochbergGND, Klaus DorerGND |
|---|---|
| Publisher: | IEEE |
| Place of publication: | Birmingham |
| Year of Publication: | 2017 |
| Pagenumber: | 6 |
| Language: | English |
| Parent Title (English): | Proceedings of the 12th Workshop on Humanoid Soccer Robots, 17th IEEE-RAS International Conference on Humanoid Robots |
| First Page: | 1 |
| Last Page: | 6 |
| Document Type: | Conference Proceeding |
| Institutes: | Bibliografie |
| Acces Right: | Frei zugänglich |
| Release Date: | 2018/01/19 |
| Licence (German): |  Es gilt das UrhG |
| URN: | urn:nbn:de:bsz:ofb1-opus4-26557 |
| URL: | http://lofarolabs.com/events/robocup/ws17/papers/Humanoids_RoboCup_Workshop_2017_pape_4.pdf |