Book Image

Artificial Vision and Language Processing for Robotics

By : Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
Book Image

Artificial Vision and Language Processing for Robotics

By: Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

Overview of this book

Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.
Table of Contents (12 chapters)
Artificial Vision and Language Processing for Robotics
Preface

Multiple Object Recognition and Detection


Multiple object recognition and detection involves detecting and recognizing several objects within an image. This task involves labeling every single object with a bounding box and then recognizing the type of that object.

Because of this, there are many available pre-trained models that detect a lot of objects. The neural network called YOLO is one of the best models for this specific task and works in real time. YOLO will be explained in depth in the next chapter for the development of the simulator for the robot.

For this chapter, the YOLO network that we want to use is trained to recognize and detect 80 different classes. These classes are:

person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop_sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, ...