Vol. 6, Issue 9, Part C (2020)

Abstract
Object Detection is a domain that has witnessed great success in the field of computer visions. It deals with images to identify and locate objects. In the real world this is of great use and is used to identify objects of class humans, trees, cars and so on. The initial tasks of object detection are started by the category detection which further leads to category recognition. The state-of-art techniques are used to enhance the accuracy with real time performance. This paper presents a review of techniques for object detection that is self-contained with deep learning and computer vision capabilities. By the use of certain models and python libraries for object detection this technique overcomes certain drawbacks of deep learning. The data sets used for training the model are most challenging and publicly available. After the study of certain relevant literature about object detection all the necessary comments are drawn.