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 …

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 …

Object detection is a fundamental ability for robots interacting within an environment. While
stunningly effective, state-of-the-art deep learning methods require huge amounts of labeled …

This paper proposes an enhancement of convolutional neural networks for object detection
in resource-constrained robotics through a geometric input transformation called Visual …

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 …

Vision-based object detection is essential for a multitude of robotic applications. However, it
is also a challenging job due to the diversity of the environments in which such applications …

Object detection models based on convolutional neural networks (CNNs) demonstrate
impressive performance when trained on large-scale labeled datasets. While a generic …

Robots working in unstructured environments must be capable of sensing and interpreting
their surroundings. One of the main obstacles of deep-learning-based models in the field of …

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 …

Vision systems are essential in building a mobile robot that will complete a certain task like
navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot …