This project mainly focuses on implementing real-time objects detection using YOLOv5, an innovative deep learning model. The aim is to detect and classify objects in live video streams or images with high accuracy and reliable. The project setting up the YOLOv5 environment ad training the model on a custom dataset and locate it for real-time deduction. Some applications include industrial automation, autonomous driving and surveillance. This project indentifies YOLOv5 efficiency, adaptability and creating it an ideal choice for developers and researchers aiming to perform real-time object detection into their system.