Detect road anomalies such as cracks, potholes, and bumps using our trained YOLOv8 models with visual demo. Real-time detection via Streamlit and Flask app

Road Damage Detection Based on Unsupervised Disparity Map Segmentation (T-ITS)

Detect damages like cracks and potholes of roads

基于YOLO模型的路面缺陷检测（Road Damage Detection）

Integrated real-time data analytics for optimized public transport, innovative road monitoring using demand prediction, and conditioning tech for sustainability, real time pothole detection either by image or video, smart parking count system for efficiency using AI/ML.

An application for automatic road damage assessment using semantic segmentation on high-resolution images. The project helps municipal authorities and maintenance teams detect and prioritize road repairs, improving safety and reducing costs.

YOLOv7-based road damage detection system trained on RDD2022 multi-national dataset. Achieves 63.85% mAP for detecting cracks and potholes. Includes Gradio UI.

Real-time pothole detection Android application using computer vision, OpenCV, and native C++ processing. Developed during a designathon that led to an Ericsson internship.

An road-anomaly detection system designed to automatically identify various types of road damage

Road Damage Detection System using Deep Learning (MobileNetV2) to classify roads into: Clean, Crack, and Pothole.

🅿️ Streamline parking management with our AI-driven platform featuring real-time tracking, user dashboards, and intelligent video analysis.

Smart City Solution: AI-powered road infrastructure monitoring | YOLOv8 object detection + Leaflet.js mapping + real-time analytics | Urban Tech Innovation

Deep Learning-based road damage detection and classification. Features a CNN pipeline for image analysis, a web-based monitoring dashboard, and an integrated messaging service for real-time safety alerts.

This project is an AI-powered pothole monitoring platform that detects road defects using YOLO, allows citizens to report issues, and provides administrators with a dashboard to track and resolve them. It combines computer vision, backend automation, and user interfaces to make road maintenance faster and smarter.

Road Damage Detection Web Based App. Project for SPARK Telkomsel 2025 Competition

YOLO-based road anomaly detection project with a Streamlit app, packaged model weights, archived evaluation artifacts, and ready-to-run local setup.

This project is an AI-powered pothole monitoring platform that detects road defects using YOLO, allows citizens to report issues, and provides administrators with a dashboard to track and resolve them. It combines computer vision, backend automation, and user interfaces to make road maintenance faster and smarter.

An advanced YOLOv8 object detection pipeline trained on mixed BDD100k & RDD datasets. Features custom stratification, live spatial mapping, and a three-tier risk assessment classifier optimized