YOLO: Real-Time Object Detection Explained

Object detection is an advanced form of image classification where a neural network predicts objects in an image and points them out in the form of bounding boxes. Object detection thus refers to the detection and localization of objects in an image that belong to a predefined set of classes. Tasks like detection, recognition, or localization find widespread applicability in real-world scenarios, making object detection (also referred to as object recognition) a very important subdomain of Computer Vision. After reading this article, you'll understand the following: Popular two-step algorithms like Fast-RCNN and Faster-RCNN typically use a Region Proposal Network that proposes regions of interest that might contain objects. The output from the RPN is then fed to a classifier that classifies the regions into classes.