Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

968

Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning.

Chanjun Chun1, Seung-Ki Ryu1

  • 1Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Korea.

Sensors (Basel, Switzerland)
|December 18, 2019
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Novelty Detection in Underwater Acoustic Environments for Maritime Surveillance Using an Out-of-Distribution Detector for Neural Networks.

Sensors (Basel, Switzerland)·2026
Same author

Contrastive Speaker Representation Learning with Hard Negative Sampling for Speaker Recognition.

Sensors (Basel, Switzerland)·2024
Same author

Effective Zero-Shot Multi-Speaker Text-to-Speech Technique Using Information Perturbation and a Speaker Encoder.

Sensors (Basel, Switzerland)·2023
Same author

A Pre-Training Framework Based on Multi-Order Acoustic Simulation for Replay Voice Spoofing Detection.

Sensors (Basel, Switzerland)·2023
Same author

Conformer-Based Dental AI Patient Clinical Diagnosis Simulation Using Korean Synthetic Data Generator for Multiple Standardized Patient Scenarios.

Bioengineering (Basel, Switzerland)·2023
Same author

Sound Event Localization and Detection Using Imbalanced Real and Synthetic Data via Multi-Generator.

Sensors (Basel, Switzerland)·2023

Road surface damage detection using fully convolutional neural networks (CNN) and semi-supervised learning improves safety. This method accurately segments road defects, reducing accident risks.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Road Engineering

Background:

  • Road pavement defects are a significant cause of traffic accidents.
  • Effective detection and countermeasures for road surface damage are crucial for road safety.

Purpose of the Study:

  • To propose a novel method for road surface damage detection using fully convolutional neural networks (CNN) and semi-supervised learning.
  • To enhance the performance of road surface damage detection techniques.

Main Methods:

  • Collected a training dataset of 40,536 images from vehicle-mounted cameras.
  • Trained a CNN model using semantic segmentation with a deep convolutional autoencoder.
  • Augmented the dataset for brightness variations.
  • Utilized semi-supervised learning with pseudo-labeled images to update the CNN model.
Keywords:
autoencoderconvolutional neural networkroad surface damagesemantic segmentationsemi-supervised learning

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K

Related Experiment Videos

Last Updated: Jan 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

968
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K

Main Results:

  • The proposed method successfully segmented road surface damages.
  • Evaluated using 450 datasets and expert assessments, confirming effectiveness.
  • Demonstrated improved performance through semi-supervised learning.

Conclusions:

  • The developed CNN-based approach with semi-supervised learning is effective for road surface damage detection.
  • This technique contributes to enhancing road safety by identifying pavement defects.
  • The method shows potential for real-world application in road maintenance and accident prevention.