💡[Feature]: Track Fault Detection

[sreevidya-16]

Is there an existing issue for this?

• [X] I have searched the existing issues

Feature Description

• The project aims to enhance railway safety by detecting faults in railway tracks through the application of deep learning algorithms.
• By leveraging advanced techniques in computer vision and neural networks, the project seeks to identify and classify defects in tracks, which can prevent accidents and ensure smoother operations.
• Utilizing deep learning models, the project will accurately pinpoint and diagnose track anomalies, automating the fault detection process and significantly reducing the time and effort required for manual inspections.
• This approach ultimately contributes to a more reliable and efficient railway system.

@sanjay-kv, Could you please assign me this issue under GSSOC'24

Use Case

Objective: To automatically detect and classify faults in railway tracks using deep learning algorithms, ensuring timely maintenance and preventing accidents.

Actors:

• System: The deep learning-based fault detection system.
• Maintenance Team: Railway maintenance engineers and workers.
• Railway Operators: Personnel responsible for train operations and scheduling.

Preconditions:

• The deep learning model is trained and deployed.
• The system is integrated with the railway's monitoring infrastructure.
• The dataset of railway track images is available for processing.

Main Flow:

1. Data Collection: Cameras installed along railway tracks continuously capture images of the tracks.
2. Preprocessing: The captured images are preprocessed to enhance quality and remove noise.
3. Fault Detection: The preprocessed images are fed into the deep learning model, which analyzes them for potential faults.
4. Classification: The model classifies any detected anomalies into different types of faults (e.g., cracks, misalignments, obstructions).
5. Alert Generation: Upon detecting a fault, the system generates an alert, including the type of fault and its location.
6. Notification: The system sends the alert to the maintenance team and railway operators via a dedicated dashboard or mobile application.
7. Action: The maintenance team assesses the alert and schedules necessary repairs to address the detected faults.
8. Follow-up: After repairs, follow-up inspections are conducted to ensure that the faults have been resolved.

Benefits

• Increased Safety: Early detection and repair of faults prevent accidents, ensuring safer travel for passengers.
• Efficiency: Automated fault detection reduces the time and effort required for manual inspections.
• Cost Savings: Timely maintenance prevents severe damage to tracks, lowering repair costs.
• Data-Driven Decisions: Historical data on faults and repairs help in predictive maintenance and better resource allocation.

Add ScreenShots

No response

Priority

High

Record

• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [X] I want to work on this issue

[github-actions[bot]]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

Note: This repo is for beginners to learn and start with Opensource we won't accept more than 10 issues from a single person, This restriction applies to Gssoc project which has a similar kind of adding folder files, Points will be reduced when we find Spam.

I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.

[sanjay-kv]

you already crossed 150 points, i dont recommned contributing more documentation. Any technical work you can still contribute to it

[github-actions[bot]]

Hello @sreevidya-16! Your issue #767 has been closed. Thank you for your contribution!