Object detection is a very important aspect of multimedia applications. Object detection algorithm can help automatically detect cattle movements, traffic signals, and road lanes for self-driving vehicles to reach their destinations. The main goal of object detection is to scan digital images or real-life scenarios to locate instances of every object, separate them, and analyze their necessary features for real-time predictions Over the past two decades, computer vision has received a great deal of coverage. Object recognition and object detection are sub fields of computer vision, the task of giving computers the ability to perceive and respond to the world around them. This is a very useful technology and has many The recent innovations that are being deployed in the current era with the latest trends in technological advancements have made researchers and scientists develop systems that are capable of identifying objects using various machine learning and deep learning algorithms. Hence, in this work, real time object detection and classification using YOLO (You Look Only Once) is presented. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLOv3(version 3) algorithm for the purpose of detecting and classifying objects. The performance of presented algorithm is validated in terms of Accuracy, Sensitivity and Specificity.