The project revolves around the application of Deep Learning for road object detection. Utilizing the power of the Faster R-CNN model with ResNet-50 backbone, our application is now capable of detecting various types of objects on roads - from pedestrians and cyclists to potholes and construction barriers. The detection classes are based on the COCO dataset and include over 80 different classes. 👨‍💻 The solution is built using Python, with the deep learning aspects leveraging PyTorch,OpenCV>K . To enhance real-time performance, we've implemented multi-threading techniques and incorporated both image and video processing capabilities. Some exciting features of our project include: - 👁️ Real-time object detection with live video feed using OpenCV - 📸 Single image object detection - 🎞️ Video processing for object detection - 🖼️ Live window capture with object detection overlay - 🛠️ Interactive GUI for easy navigation