Real-time deep learning-based object recognition.

Scene interpretation, video surveillance, robots, and autonomous driving systems are just a few examples of the many areas where computer vision has been put to use in the last decade, inspiring a plethora of academic study in the area. In recent years we have seen a surge in interest in the study of visual recognition systems, which can do things like classify, localize, and locate objects in images. In essence, these apps would not function without these systems. Significant developments in neural networks have allowed these algorithms to attain outstanding performance. There have been several developments in areas like object recognition thanks to the growth of computer vision. Also, the several deep learning frameworks and services that may be used for object recognition are detailed. In this research, we compare and contrast several deep learning methods used in state-of-the-art object recognition systems. A video's frames will be run through an object recognition algorithm as part of this research. This will allow the algorithm to identify anything in the video, including but not limited to humans, vehicles, animals, and other objects. Both in computer vision and autonomous driving systems, object recognition and detection play crucial roles. Our goal is to build a system that offers solutions in real time, does not skimp on speed, and does not compromise on accuracy. The significance of computer vision is increasing on a daily basis, and as a consequence, models that provide high-performance solutions are becoming more prevalent. The exponential expansion in computer power, in conjunction with the rising interest in deep learning, has resulted in a significant rise in the number of high-performance algorithms that are able to tackle issues that occur in the real world. Our approach has the potential to be improved by providing the user with the freedom to identify just those things that are necessary at the present time as per dataset it is undergoing training on a more comprehensive dataset. [ABSTRACT FROM AUTHOR]