The RoboCup Soccer is one of the largest initiatives in the robotics field of research. This initiative considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. This work evaluates deep neural networks for the detection of the ball and robots. For such task, five convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an mAP of 0.98 and 14.7 frames per second, running on CPU

The RoboCup Soccer is one of the largest competitions in the robotics field of research. It considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. In this paper, an approach using Convolutional Neural Networks for object detection is described. The soccer ball is the chosen object and three state-ofart convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an average precision of 0.972 with an intersection over union of 50% and 9.64 frames per second, running on CPU.