Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results.

Humans can easily detect and identify objects present in an image. The human visual system is fast and accurate and can perform complex tasks like identifying multiple objects and detecting obstacles with little conscious thought. With the availability of large amounts of data, faster GPUs, and better algorithms, we can now easily train computers to detect and classify multiple objects within an image with high accuracy. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them. In this guide, you’ll find answers to all of those questions and more. Whether you’re an experienced machine learning engineer considering implementation, a developer wanting to learn more, or a product manager looking to explore what’s possible with computer vision and object detection, this article is for you.

Specific to this example, we have an object in the image. We can create a bounding box around the object and this object is an emergency vehicle. Now the object detection problem can also be divided into multiple categories. First is the case when you have images that have only one object. That is you can have 1000 images in the data set, and all of these images will have only one object. And if all these objects belong to a single class, that is all the objects are cars, then this will be an image localization problem. That is you already know what class these objects belong to, you only have to locate where these objects are present in the image.