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Related Experiment Video

Updated: Jun 22, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

A pothole video dataset for semantic segmentation.

Muhammad Ihsan¹, Muhammad Alfian Amrizal¹, Agus Harjoko¹

¹Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia.

|February 16, 2024

Summary

A new video dataset aids pothole detection. This resource supports research in road safety and computer vision by providing high-resolution videos for semantic segmentation algorithm development.

Keywords:

Artificial intelligence Computer vision Deep learning Road damage Road potholes Segmentation Sequence data

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Area of Science:

Computer Science
Civil Engineering
Geospatial Analysis

Background:

Road potholes pose significant risks to vehicle safety and infrastructure integrity.
Accurate pothole detection is crucial for road maintenance and traffic management.
Existing datasets may lack the diversity and resolution needed for robust semantic segmentation models.

Purpose of the Study:

To introduce a novel, high-resolution video dataset specifically designed for semantic segmentation of road potholes.
To facilitate the development and benchmarking of advanced pothole detection algorithms.
To support research in autonomous driving systems and intelligent transportation infrastructure.

Main Methods:

The dataset comprises 619 high-resolution MP4 videos, each two seconds long with 48 frames.
Videos were captured in January 2023 across eight villages in South Kalimantan, Indonesia.
The dataset is organized into train, validation, and test sets, with RGB video and ground truth mask subfolders.

Main Results:

The dataset provides 619 videos with corresponding ground truth masks for semantic segmentation.
It enables flexible research from full-video analysis to frame-level extraction.
The data supports the creation of new annotations and custom dataset configurations.

Conclusions:

This video dataset is a valuable resource for the computer vision and road safety research communities.
It enables benchmarking of semantic segmentation algorithms for pothole detection.
The dataset will advance the development of more effective pothole analysis and mitigation strategies.