A Deep Learning Approach for Object Recognition with NAO Soccer Robots

The use of identical robots in the RoboCup Standard Platform League (SPL) made software development the key aspect to achieve good results in competitions. In particular, the visual detection process is crucial for extracting information about the environment. In this paper, we present a novel approach for object detection and classification based on Convolutional Neural Networks (CNN). The approach is designed to be used by NAO robots and is made of two stages: image region segmentation, for reducing the search space, and Deep Learning, for validation. The proposed method can be easily extended to deal with different objects and adapted to be used in other RoboCup leagues. Quantitative experiments have been conducted on a data set of annotated images captured in real conditions from NAO robots in action. The used data set is made available for the community. © 2017, Springer International Publishing AG