Real-time object detection is a difficult task that has drawn a lot of interest in the deep learning community. Object detection algorithms are frequently employed in robotics, security, and autonomous car applications. In this abstract, we suggest a novel deep learning method for real-time object detection. You Only Look Once (YOLO) and Faster R-CNN (Region-based Convolutional Neural Network), two well-known deep learning architectures, are the foundation of our suggested solution. The Faster R-CNN design is renowned for its accurate object localisation, whereas the YOLO architecture is noted for its speed and accuracy in object recognition. In order to quickly locate potential object regions in the input image, we suggest using the YOLO architecture. After that, a Faster RCNN network is used to accurately localise the items within these candidate regions. We can perform realtime object detection with high accuracy and exact localisation by fusing the benefits of these two systems. We offer a novel loss function that combines the YOLO and Faster R-CNN loss functions in order to substantially boost the performance of our method. With the use of this loss function, we can train our network to simultaneously optimise for speed and accuracy, creating a more effective system for object detection. Our suggested method has been rigorously tested on numerous datasets, and the findings demonstrate that it performs better in terms of speed and accuracy than cutting-edge object detection algorithms. We think that our strategy has the potential to revolutionise the realtime object identification industry and open the door for the creation of fresh, cutting-edge applications.