[CV] Introduction to Object Detection
A 2019 Guide to Object Detection 1) Introduction Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds. Object detection has been applied widely in video surveillance , self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. Object detection usually involves two processes; classifying and object’s type , and then drawing a bounding box . Common object detection model architectures: R-CNN Fast R-CNN Faster R-CNN Mask R-CNN (todo) SSD (Single Shot MultiBox Defender) YOLO (You Only Look Once) Objects as Points (todo) Data Augmentation Strategies for Object Detection (todo) 2.1) Why not use standard CNN? The major reason why you cannot proceed with this