• search hit 37 of 49
Back to Result List

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.show moreshow less

Export metadata

Metadaten
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:Hochschule Offenburg / Bibliografie
Acces Right:Frei zugänglich
Release Date:2018/01/19
Licence (German):License LogoEs 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