Special Issue "Object Detection using Deep Learning for Autonomous Intelligent Robots"
Deadline for manuscript submissions: 30 April 2019
Autonomous intelligent robots are dynamic systems consisting of an electronic controller coupled to a mechanical body, with the need of an adequate sensory system to perceive the environment where they operate. Digital cameras are one of the most commonly used sensors at the moment.
As these robots move towards more complex environments and applications, image-understanding algorithms that allow a more precise and detailed object recognition become crucial. In this context, in addition to classifying images, it is also necessary to precisely estimate the class and location of objects contained within the images, a problem known as object detection.
The most important advances in object detection were achieved due to improvements in object representation and machine learning models. In the last few years, deep neural networks (DNNs) have emerged as a powerful machine-learning model. They are deep architectures which have the capacity to learn powerful object representations/models without the need to manually design features.
Usually, we associate the use of deep learning with high-complexity processing systems, and this is a challenge when we think of how to use it in autonomous intelligent robots. However, recent advances in single-board computers and networks allow the use of these technologies in real-time on these types of intelligent systems.
The main aim of this Special Issue is to present novel approaches and results focusing on deep-learning approaches for the vision systems of intelligent robots. Contributions that explore both on-board implementations or distributed vision systems with modules running remotely from the robot are welcome.
Prof. Dr. António J. R. Neves
Manuscript Submission Information
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- Deep leaning
- Neural networks
- Object detection
- Image processing
- Real-time systems
- Autonomous robots
- Intelligent robots