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.

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.

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

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

AI-powered road damage detection and road health assessment using YOLO, Streamlit, and GPS visualization.

Transfer learning benchmark: ResNet-50, EfficientNet-B3, ViT-B/16, MobileNet-V3 on RDD2022 Japan pavement distress dataset — accuracy, calibration, latency, and parameter count evaluated.

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

YOLOv8 road damage detection pipeline with MLflow tracking and production-style MLOps workflow.

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.

YOLOv8 pavement distress detection with Grad-CAM explainability — heatmaps, per-class confidence analysis, confusion matrix, and Streamlit dashboard

AI-powered road damage detection and reporting system using YOLO, Flask, React, SQLite, OpenStreetMap, and Leaflet.

Real-time road damage detection on Raspberry Pi 5 using YOLOv8 Nano with NCNN and Hailo-8 backends. Engineering thesis — AGH UST 2026.

Ai powered smart road damager detection and repair cost estimator and citizen reporting software

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