The need for real-time object recognition is growing in a few applications, including robotics, surveillance, and autonomous vehicles. Modern object recognition technique YOLOv5 achieves high accuracy while maintaining real-time performance. This paper proposes a real-time, highly accurate object recognition method using YOLOv5. The system, which was created using PyTorch and Python, is trained and evaluated using the COCO dataset. The proposed system enables fast object detection and achieves outstanding precision and recall rates using single-shot detector architecture. Additionally, the detection accuracy is greatly improving with the introduction of YOLO and its architectural descendants. YOLOs are frequently employed in a variety of contexts, mostly because of their speedy conclusion rather than due to the accuracy of their detection. The YOLO detection accuracy, for instance, ranges between 63.4 and 70. The suggested system is ideal for real-time object detection applications since experimental findings demonstrate that it performs better than current object detection systems in terms of accuracy and speed.