Convolutional neural networks (CNNs) are the state-of-the-art method for most computer vision tasks. But, the deployment of CNNs on mobile or embedded platforms is challenging because of CNNs’ excessive computational requirements. We present an end-to-end neural network solution to scene understanding for robot soccer. We compose two key neural networks: one to perform semantic segmentation on an image, and another to propagate class labels between consecutive frames. We trained our networks on synthetic datasets and fine-tuned them on a set consisting of real images from a Nao robot. Furthermore, we investigate and evaluate several practical methods for increasing the efficiency and performance of our networks. Finally, we present RoboDNN, a C++ neural network library designed for fast inference on the Nao robots.
CITATION STYLE
Szemenyei, M., & Estivill-Castro, V. (2019). Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11374 LNAI, pp. 96–108). Springer Verlag. https://doi.org/10.1007/978-3-030-27544-0_8
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