Road Defect Detection (Images)

Overview

An intelligent system that uses computer vision and deep learning to automatically detect and classify various types of road defects from images. The system was specifically designed for Addis Ababa, Ethiopia, helping in early identification of road maintenance needs and infrastructure planning.

Key Features

• Real-time Defect Detection: Captures and analyzes road imagery, identifying potholes, cracks, and other surface defects using custom-trained neural networks
• GPS Integration: Automatically geotags detected defects for precise mapping and maintenance planning
• Mobile Data Collection: Raspberry Pi-based hardware solution that can be mounted on vehicles for automated road surveying
• Interactive Dashboard: Web interface for viewing defect locations, severity, and analytics with Google Maps integration
• Secure User Management: Role-based access control for data management and system administration

Technical Implementation

• Image Processing Pipeline: Custom OpenCV-based preprocessing to enhance image quality and isolate defects
• Deep Learning Model: Convolutional Neural Network trained on a dataset of road defects specific to Ethiopian infrastructure
• Hardware Integration: Raspberry Pi system with camera module and GPS sensors for field data collection
• Backend Infrastructure: Flask-based web server with MongoDB database for defect data storage and retrieval

Impact

• Reduced road maintenance response time by providing accurate, actionable data on defect locations
• Enhanced infrastructure planning through data-driven insights on road condition patterns
• Improved road safety by enabling proactive identification of potentially hazardous road conditions