Lane Detection Web Application using DeepLabv3 Model
The Lane Detection Web Application is a Django-based web application that utilizes the DeepLabv3 model for accurately detecting and marking lanes on images or videos. It provides users with an interactive interface to upload road scenes and visualize the detected lanes in real-time.
Image Processing, Video Processing
This project focuses on the development of a web application that utilizes the DeepLabv3 model for lane detection and marking. The application is built using the Django framework and leverages the power of PyTorch for training the DeepLabv3 model. It provides users with an interactive interface to upload images or videos and visualize the detected lanes.
The Lane Detection Web Application using the DeepLabv3 Model is designed to accurately detect and mark lanes on images or videos. The application utilizes the DeepLabv3 model, which has been trained on a large dataset of road images, to identify lane boundaries and differentiate them from the surrounding environment.
The Django framework is used to develop the web application, providing a robust and scalable solution for handling user requests and managing the application's backend. The application allows users to upload images or videos containing road scenes. Once the input is provided, the DeepLabv3 model processes the data and detects the lane markings.
The detected lanes are then marked on the uploaded images or videos, providing a visual representation of the lane boundaries. The application displays the processed images or videos to the user, enabling them to observe the detected lanes in real-time. Users can also download the marked images or videos for further analysis or sharing.
Django, Pytorch, OpenCV