Since its inception in 1997, RoboCup has developed into a truly unique and long-standing
research community advancing robotics and artificial intelligence through various …

In most applications for autonomous robots, the detection of objects in their environment is of
significant importance. As many robots are equipped with cameras, this task is often solved …

With the emergence of onboard vision processing for areas such as the internet of things
(IoT), edge computing and autonomous robots, there is increasing demand for …

Ball detection is one of the most important tasks in the context of soccer-playing robots. The
ball is a small moving object which can be blurred and occluded in many situations. Several …

Detecting ball is the first competence that should be owned by robot soccer. In the case of
wheeled-based robot soccer, the existence of a ball is commonly detected by using an …

We present a dataset specifically designed to be used as a benchmark to compare vision
systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection …

Abstract Convolutional Neural Networks (CNNs) have shown promising results for various
computer vision tasks. Despite their success, localizing the ball in real-world RoboCup …

Deep Learning has become exceptionally popular in the last few years due to its success in
computer vision [1–3] and other fields of AI [4–6]. However, deep neural networks are …

The goal of this paper is to propose a vision system for humanoid robotic soccer that does
not use any color information. The main features of this system are:(i) real-time operation in …

This work presents a study for building a Deep Vision pipeline suitable for the Robocup
Standard Platform League, a humanoid robot soccer tournament. Specifically, we focus on …