Combining YOLO and R-CNN for Robust Object Detection

This research focuses on the development of a real-time object detection system using deep learning techniques. The system is designed to accurately identify and localize multiple objects within video frames in real-time, making it suitable for applications such as surveillance, autonomous vehicles, and smart environments. Real-time object is a complex area and fundamental of computer vision. Due to its increased utilization in face recognition, tracking system, robotics, augmented reality and surveillance used in security and many others applications like live streaming filters (Snapchat, Instagram). The goal is to identify objects Which is done with the help of YOLO to locate object using bounding boxes [1]. YOLO (You Only Look Once) is a new approach to object detect, it outperforms other detection methods, including DPM and R-CNN when generalizing from natural images to other domains like artwork. In recent years, the integration of real- time object detection with edge computing and IoT has gained traction. This combination allows smart devices to make on-the spot decisions without relying heavily on cloud services enhancing privacy and reducing latency. [2]It is briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, Besides, this paper contributes a lot to YOLO and other object detection literature.