When we’re shown an image, our brain instantly recognizes the objects contained in it. On the other hand, it takes a lot of time and training data for a machine to identify these objects. But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive.
Check out the below image as an example. The system is able to identify different objects in the image with incredible accuracy.
Object detection technology has seen a rapid adoption rate in various and diverse industries. It helps self-driving cars safely navigate through traffic, spots violent behavior in a crowded place, assists sports teams analyze and build scouting reports, ensures proper quality control of parts in manufacturing, among many, many other things. And these are just scratching the surface of what object detection technology can do!
In this article, we will understand what object detection is and look at a few different approaches one can take to solve problems in this space. Then we will deep dive into building our own object detection system in Python. By the end of the article, you will have enough knowledge to take on different object detection challenges on your own!
Note: This tutorial assumes that you know the basics of deep learning and have solved simple image processing problems before. In case you haven’t, or need a refresher, I recommend reading the following articles first:
Fundamentals of Deep Learning – Starting with Artificial Neural Network
Deep Learning for Computer Vision – Introduction to Convolution Neural Networks
Tutorial: Optimizing Neural Networks using Keras (with Image recognition case study)
For more :
https://www.analyticsvidhya.com/blog/2018/06/understanding-building-object-detection-model-python/
