pothole-detection

Easy-to-use finetuned YOLOv8 models.

Pothole Detection || Pothole Detection using python and deep learning

Pothole Detection using Ultralytics YOLOv8.

AI-powered real-time object detection, tracking, and video intelligence platform using YOLO and computer vision.

Attention Aggregation Framework in PyTorch, ECCV Workshops 2020

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

🥇 1st place winner | Bump.IT - Pothole detection and mapping. Using data science methods of analysis, mobile phone's telemetry, computer vision, and, deployed through Azure.

Spothole Core Backend (Object Detection + Flask API) - Artificial Intelligence Powered Pothole Detection, Reporting and Management Solution

This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

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

Dataset accompanying the paper titled "Pothole detection and dimension estimation system using deep learning (YOLO) and image processing"

A mobile application made in Flutter that is capable of detecting potholes in real-time and alerting the driver to avoid any accidents caused by potholes. The app also estimates approx dimensions of the pothole to measure the severity may caused upon accident.

Helping navigate through maps to prefer road-way.

Pothole detection using image processing scheme

The Model will detect whether the image consists of potholes or not. If the image consists of pothole then it will detect the total number of potholes in the image as well as it will assign them a level.

Community Pothole Tracker

Using deep learning and transfer learning techniques to differentiate plain roads and those with potholes using three different classifiers to obtain the best accuracy with the same convolutional base

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.

AI-powered real-time road hazard detection system that identifies potholes, speed breakers, and other obstacles using YOLOv8 and computer vision — ensuring safer, smarter, and privacy-preserving mobility.