Object detection is a fantastic technology of machine learning, and many organizations use it for their benefit. The face recognition system in your phone, driverless cars, and the crowd statistics, they all have one thing in common: they use object detection.

If you’re learning machine learning, you’d surely want to get familiar with this technology. In this article, you’ll learn about TensorFlow object detection and how you can perform the same. Be sure to check the example as you can try it out yourself and experiment.

What is Object Detection?

The name of ‘Object Detection’ is self-explanatory. It refers to finding real objects in images (or videos). These objects could be cars, TVs, or even humans. With object detection, you can localize, recognize, and detect multiple objects in an image. Object detection finds applications in many industries. From surveillance to product quality inspection, you’d find plenty of areas where experts use this technology. You can perform object detection in the following ways:

• Deep learning method
• Viola-Jones method
• Feature-based detection
• SVM classifications (through HOG features)

In this article, we’ll focus on TensorFlow object detection. And TensorFlow performs this through deep learning. So in our tutorial, we’ll use that method.

TensorFlow: An Introduction

It’s also important to be familiar with what TensorFlow is. It’s an Open Source Machine Learning framework and a product of Google. You can use TensorFlow to perform dataflow programming. Its name is made up of ‘Tensor’ and Flow’, both of which are vital components of this technology. Tensors are multidimensional arrays, whereas the flow stands for the dataflow programming you perform.

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