Object Identification Using Deep Learning

Human visual system is very fast that he can easily detect and identify the objects using this visual system it can easily identify multiple objects and detect the obstacle. This time there is a very large amount of data, a faster CPU and good algorithm that can easily train computers to detect and classify multiple objects with high accuracy. It uses an algorithm to detect and identify the objects that is “You Only Look Once” (YOLO) algorithm. This project is based on a deep learning approach to solve the problem of object detection and recognition. This network is trained on the most challenging dataset known as (PASCAL VOC), in which object detection is conducted annually manner. The resulting system is very fast and accurate. Object recognition is a collection of related tasks for identifying objects in digital photograph. R-CNN is a family of techniques for addressing object localization and recognition tasks. Propose a new object detection method which improves the existing model at every stage of object detection and identification, use COCO dataset model in this lot of categories of images are stored.