road-damage

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

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

A machine learning project designed to detect road damage from images, leveraging deep learning and computer vision techniques for efficient and accurate damage detection.

Road damage detection with YOLO26 on RDD2022: cracks and potholes across countries, with a Norway fine-tuning study.

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

AI Pothole Detection and Mapping with YOLO (RTX 4050 + Colab T4)

🦋 — A web-based platform designed for reporting road damage with a dashboard and various other useful features to streamline the process of public reporting.

Detección de baches y grietas en fotos y video con YOLO26 (RDD2022), índice de severidad y seguimiento por tramo. FastAPI, PostgreSQL y app Android.

Detecting cracks, potholes and manholes with YOLOv8s, YOLO11s and YOLO26s — trained, compared on the same data and hardware, and deployed with FastAPI.

RT-DETR vs YOLO for road damage across countries, turned into repair priorities on a map; TensorRT, Prometheus/Grafana, MLflow gate