What is Object Detection?

Object Detection is a computer vision technique that is used for locating objects in a digital image or video, and identifying (or classifying) them. It can be done using single-stage approaches as well as two-stage. Each approach has its pros and cons. Typically, the two stages of object detection are:

1. Extracting Regions of Interest (RoI) by generating candidate boxes.
2. Classifying the RoIs

Region Proposal Network (RPN) is used in the first step to generate proposals of regions of interest, where the model extracts potential candidates of objects in the image or video. It basically tells the network where to look. Traditionally, this was done using computer vision techniques like selective search, which were computationally expensive. RPN became extremely popular due to its power as well as faster computation. It helped in reducing the running time of detection networks like Fast RCNN and SPPnet. Let us understand how it works.

In recent times Object Detection Algorithms have evolved manifold and this has led to many advancements in the applications which helped us solve real-world problems with the utmost efficiency and latency of real-time. In this article, we will look a Region Proposal Networks which serve as an important milestone in the advancements of Object Detection Algorithms.