Robust Object Detection and Recognition

Abstract

Object detection and recognition has attained a lot of attention in image processing field. In all of these a very important role is played by a robot and other artificial intelligent software which provide us with different services like detecting objects and performing their tasks like a human being and also provide us with security. In this work, we develop a high-confidence region-based object detection framework that boosts up the classification performance with less computational burden. In order to formulate our framework, we consider a deep network that activates the semantically meaningful regions in order to localize objects. For this purpose our work is going to participate in all those ways to detect robust objects and recognize them efficiently so that this can be used in robotics and other artificial intelligence services. In many organizations like companies, hospitals or shopping malls we need to detect object for security purposes. Robot in artificial intelligence software play a very important role which provide us different services like detecting objects.