Feature request: Add road condition monitoring project using Deep Learning

[Tusharb331]

Addition of an AI Vision-Based Solution for Road Condition Monitoring

The solution aims to automate the detection and classification of road surface defects (such as potholes, cracks, etc.) using deep learning techniques like Convolutional Neural Networks (CNNs).

Is your feature request related to a problem? Please describe.

The manual process of monitoring road conditions is time-consuming, resource-intensive, and prone to human error. It often results in delayed detection of surface defects like potholes and cracks, leading to increased maintenance costs and unsafe driving conditions. An automated solution can streamline this process, providing faster and more accurate road condition assessments.

Describe the solution you'd like

An AI-based system that uses Convolutional Neural Networks (CNNs) to analyze images or video feeds of road surfaces and automatically detect and classify different types of road surface defects (e.g., potholes, cracks, uneven surfaces). This system could be integrated with existing surveillance cameras or deployed using drones/vehicles equipped with cameras for real-time condition monitoring. The output would include defect detection reports, classification, and severity level assessment, enabling quicker maintenance decisions.

Describe alternatives you've considered

1. Manual Inspection: Utilizing human inspectors to survey road conditions, but this is slow and expensive.
2. Traditional Computer Vision Techniques: Using edge detection or other traditional methods for defect detection, which might not perform well under varying lighting and weather conditions.
3. Sensor-Based Monitoring: Using sensors to detect road vibrations and defects, but this requires installation on multiple vehicles and may not provide as detailed information as image-based methods.

Additional context

This solution can significantly improve road safety by providing quick alerts about dangerous conditions. It also helps in optimizing maintenance efforts, reducing costs, and ensuring a smoother driving experience. The AI-based approach can work in various weather conditions and adapt to different road types. The system can be further improved with continuous learning using new data collected over time.

[github-actions]

Thanks for creating the issue in ML-Nexus!🎉
Before you start working on your PR, please make sure to:

• ⭐ Star the repository if you haven't already.
• Pull the latest changes to avoid any merge conflicts.
• Attach before & after screenshots in your PR for clarity.
• Include the issue number in your PR description for better tracking.
  Don't forget to follow @UppuluriKalyani – Project Admin – for more updates!
  Tag @Neilblaze,@SaiNivedh26 for assigning the issue to you.
  Happy open-source contributing!☺️

[github-actions]

Thanks for raising this issue! However, we believe a similar issue already exists. Kindly go through all the open issues and ask to be assigned to that issue.

[github-actions]

Hey @Tusharb331, can you share the progress of this project?

[github-actions]

This issue has been automatically closed because it has been inactive for more than 30 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you!