Lane_detection Computer Vision Project

Here are a few use cases for this project:

1. Assisting Autonomous Vehicles: The "Campus" computer vision model can be integrated into the software of self-driving cars to help them recognize and understand road signs, enhancing navigation and ensuring adherence to traffic rules. This will improve the safety and reliability of autonomous vehicles on the road.

2. Smart Traffic Management Systems: The model can be implemented in city-wide traffic management systems to monitor and analyze real-time traffic conditions. By detecting and interpreting street signs, the system can adapt traffic signals, control the flow of vehicles, and reduce congestion for a more efficient and eco-friendly urban transport system.

3. Enhancing Mobility for Visually Impaired Individuals: The "Campus" computer vision model can be integrated into assistive technologies for visually impaired individuals, such as specialized glasses, smart canes, or smartphone apps. By detecting street-signs, it can provide these users with important information about their environment and assist them in navigating the urban landscape safely.

4. Augmented Reality Navigation Apps: The model can be incorporated into AR-based smartphone navigation applications, enabling users to see helpful overlays of street sign information in real-time, making it easier to follow directions, obey traffic laws, and maintain safety while commuting.

5. Road Infrastructure Maintenance and Upgrades: Municipalities or local governments can use the "Campus" computer vision model to assess and analyze the condition of existing road infrastructure, identifying damaged or outdated signs through a database of images taken by drones or vehicles. This allows for efficient prioritization of repairs, replacements, or improvements, promoting both safety and cost-effectiveness in road maintenance work.