Build Various AI Models and Build Technology with Web Interface with Reports Including Mobile App (NHAI Project)

Context & Background

Highway infrastructure monitoring requires large-scale inspections of road surfaces and structures. Using vehicle-mounted cameras, state highway agencies capture thousands of hours of inspection videos that require rapid classification.

Problems to be Addressed

Processing massive video streams manually for road distress (cracks, asset degradation) is slow and subject to engineer grading errors. Highly scalable cloud pipelines are needed to process these feeds automatically.

Aims and Objectives

1. Build computer vision models for highway road asset and defect detection.
2. Implement web-based reporting interfaces and analytics dashboards.
3. Deploy mobile applications for highway inspectors in the field.

Methodology

The team designs deep learning models (YOLO, Faster R-CNN) to detect and classify road distress. Videos captured from highway project vehicles are uploaded to a cloud server, where the models generate structured geo-tagged defect reports.

Expected Outcomes

A complete highway inspection web portal, mobile reporting apps, and integration with national highway development datasets.