@inproceedings{SchnekenburgerScharffenbergWuelkeretal.2017,
  author    = {Fabian Schnekenburger and Manuel Scharffenberg and Michael W{\"u}lker and Ulrich Hochberg and Klaus Dorer},
  title     = {Detection and Localization of Features on a Soccer Field with Feedforward Fully Convolutional Neural Networks (FCNN) for the Adult-Size Humanoid Robot Sweaty},
  series = {Proceedings of the 12th Workshop on Humanoid Soccer Robots, 17th IEEE-RAS International Conference on Humanoid Robots},
  publisher = {IEEE},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:ofb1-opus4-26557},
  year      = {2017},
  abstract  = {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 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.},
  language  = {en}
}