Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

YOLO – real-time object detection


To learn how to use deep learning in OpenCV, we are going to present an example of object detection and classification based on the YOLO algorithm. This is one of the fastest object detection and recognition algorithms, which can run at around 30 fps in an NVIDIA Titan X.

YOLO v3 deep learning model architecture

Common object detection in classical computer vision uses a sliding window to detect objects, scanning a whole image with different window sizes and scales. The main problem here is the huge time consumption in scanning the image several times to find objects.

YOLO uses a different approach by dividing the diagram into an S x S grid. For each grid, YOLO checks for B bounding boxes, and then the deep learning model extracts the bounding boxes for each patch,the confidence to contain a possible object, and the confidence of each category in the training dataset per each box. The following screenshot shows the S x S grid:

YOLO is trained with a grid of...