  When producing a model to object detection in a specific context, the first
obstacle is to have a dataset labeling the desired classes. In RoboCup, some
leagues already have more than one dataset to train and evaluate a model.
However, in the Small Size League (SSL), there is not such dataset available
yet. This paper presents an open-source dataset to be used as a benchmark for
real-time object detection in SSL. This work also presented a pipeline to
train, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a
low-power embedded system. This pipeline was used to evaluate the proposed
dataset with state-of-art optimized models. In this dataset, the MobileNet SSD
v1 achieves 44.88% AP (68.81% AP50) at 94 Frames Per Second (FPS) while running
on an SSL robot.
