An Analysis Study on the Different Approaches of Deep Learning for Object Recognition and Detection

Object Detection is an emerging Computer Technology which focuses on Image Processing and computer vision where the digital input image is detected for the presence of objects like human beings, animals, plants and vehicles so on. Object Detection has been around for more years and recently it has been applied in Image retrieval, Security, Automated Vehicle system, Surveillance and many more. Fast Development of Deep Learning Algorithms has induced object detection approach to a great extent. As the applications improve, the challenges also will increase. This Survey paper will discuss the main objective of the detection of object and the various improved Deep learning Algorithms used in recent years and the existing techniques for object detection, the various benchmark Datasets used in a systematic manner covering the various stages of Object detection. In this Survey, the various object detection algorithms like Deep Convolutional Neural Network, R-CNN, YOLO, Deep CNN Algorithm and more are compared with their performances and the kind of Datasets they have demonstrated are also analysed