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@InCollection{RoboCup2022-nskiran,
  author = {Sai Kiran Narayanaswami and Mauricio Tec and Ishan Durugkar and Siddharth Desai and Bharath Masetty and Sanmit Narvekar and Peter Stone},
 title = {Towards a Real-Time, Low-Resource, End-to-end Object Detection Pipeline for Robot Soccer},
 editor="Amy Eguchi and Nuno Lau and Maike Paetzel-Prussman and Thanapat Wanichanon",
 booktitle="{R}obo{C}up 2022: Robot World Cup {XXV}",
 year="2023",
 publisher="Springer International Publishing",
  pages="62--74",
  abstract = {This work presents a study for building a Deep Vision pipeline
  suitable for the Robocup Standard Platform League, a humanoid robot
  soccer tournament. Specifically, we focus on end-to-end trainable
  object detection for effective perception using Aldebaran NAO v6 robots.
  The implementation of such a detector poses two major challenges,
  those of speed, and resource-effectiveness with respect to memory and computational power. We benchmark architectures
  using the YOLO and SSD detection paradigms, and identify variants
  that are able to achieve good detection performance for ball
  detection, while being able to perform rapid inference. To add to
  the training data for these networks, we also create a dataset from
  logs collected by the UT Austin Villa team during previous competitions,
  and set up an annotation pipeline for training.
  We utilize the above results and training pipeline to realize a
  practical, multi-class object detector that enables the robot's vision system to
  run at 35 Hz while maintaining good detection performance.
  },
  wwwnote={<a href="https://link.springer.com/book/10.1007/978-3-031-28469-4">The book</a>},
}