road-damage-detection

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)

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

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

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.

Detect damages like cracks and potholes of roads

AI-powered road damage detection and severity assessment using deep learning and computer vision, with an interactive web application for analyzing road conditions.

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.

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.

YOLOv8 Nano + EigenCAM for interpretable road damage detection in intelligent transportation systems.

PROBE (WACV 2026): PyTorch research scaffold for self-supervised visual prompting in cross-domain road damage detection.

Reproducible CPU-only road damage detection on RDD2022 with domain-aware COCO evaluation and an IEEE paper

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

AI-powered ADAS pothole detection system using YOLO, Random Forest, Flask and OpenCV.

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

Pre-alpha Python and Go toolkit for computer-vision road observations, traffic forecasting, GPS/GeoJSON, map-matching contracts, and route intelligence.

PyTorch implementation of EfficientDet (D0, D2, D3) for Road Damage Detection on RDD2022 dataset. Includes K-Medoids anchors, DDP training, and COCO evaluation.

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

Smart Road Pothole Detection System using YOLOv8 with severity scoring, geospatial mapping, and Streamlit deployment. Built for ATRISI Amplify Builder Challenge.

Baseline implementation of Road Damage Object Detection using YOLOv8, YOLOv10, and YOLO11 with Hyperparameter Tuning and Streamlit deployment.