The goal of this paper is to describe a vision system for humanoid robot soccer players that does not use any color information, and whose object detectors are based on the use of convolutional neural networks. The main features of this system are the following: (i) real-time operation in computationally constrained humanoid robots, and (ii) the ability to detect the ball, the pose of the robot players, as well as the goals, lines and other key field features robustly. The proposed vision system is validated in the RoboCup Standard Platform League, where humanoid NAO robots are used. Tests are carried out under realistic and highly demanding game conditions, where very high performance is obtained: a robot detection accuracy of 94.90%, a ball detection accuracy of 97.10%, and a correct determination of the robot orientation 99.88% of the times when the observed robot is static, and 95.52% when the robot is moving.

本文的目的是描述一种不使用任何颜色信息的人形机器人足球运动员的视觉系统，并且其目标检测器基于卷积神经网络。该系统的主要特征如下：（i）在受计算限制的类人机器人中的实时操作，以及（ii）检测球的能力，机器人运动员的姿势以及球门，线和其他关键领域功能强大。拟议的视觉系统已在RoboCup标准平台联盟（使用人形NAO机器人）中得到了验证。测试是在现实而又要求很高的游戏条件下进行的，在这些条件下，它们具有非常高的性能：机器人检测精度为94.90％，球检测精度为97.10％，并且正确确定了机器人的方向99。