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 Concept Videos

Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Classification of Signals01:30

Classification of Signals

437
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
437
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.0K
Deconvolution01:20

Deconvolution

154
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
154
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.3K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

446
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
446

You might also read

Related Articles

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

Sort by
Same author

Multiple roles of circRNAs in cervical cancer: From fundamental carcinogenic mechanisms to clinical application prospects.

Critical reviews in oncology/hematology·2026
Same author

Pediatric non-gestational choriocarcinoma: systematic literature review and a multicenter case series.

Journal of gynecologic oncology·2026
Same author

Author's Reply: Letter to the Editor: Concerns Regarding Folic Acid in the Prevention and Treatment of Cervical Cancer.

Nutrition reviews·2026
Same author

Prognostic outcomes of multifocal bilateral Wilms tumor: A Chinese multicenter cohort study.

Journal of pediatric urology·2026
Same author

Efficacy of bispecific T-cell engagers after CAR T-cell therapy failure in aggressive large B-cell lymphoma.

Current research in translational medicine·2026
Same author

Clinical features and surgical challenges of advanced retroperitoneal rhabdomyosarcoma in children: a single-center 17-year experience.

Translational pediatrics·2026
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

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

1.0K

Transfer learned deep feature based crack detection using support vector machine: a comparative study.

K S Bhalaji Kharthik1, Edeh Michael Onyema2,3, Saurav Mallik4

  • 1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, 641112, India.

Scientific Reports
|June 24, 2024
PubMed
Summary
This summary is machine-generated.

Automated crack detection using transfer learned Deep Convolutional Neural Networks (DCNNs) significantly improves infrastructure integrity. This study compares DCNNs for crack classification and feature extraction, enhancing accuracy with image enhancement and Support Vector Machine integration.

Keywords:
Convolutional neural networksCrack detectionSupport vector machine (SVM)Transfer learning

More Related Videos

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

517
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Related Experiment Videos

Last Updated: Jun 23, 2025

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

1.0K
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

517
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Infrastructure integrity is crucial, with cracks posing significant risks.
  • Manual inspection methods for cracks are time-consuming and inefficient.
  • Automated crack detection using Deep Convolutional Neural Networks (DCNNs) is essential for critical infrastructure management.

Purpose of the Study:

  • To compare the effectiveness of transfer learned DCNNs for crack detection.
  • To evaluate DCNNs as both classification models and feature extractors.
  • To assess the impact of image enhancement and Support Vector Machine (SVM) integration on crack detection performance.

Main Methods:

  • Evaluated 12 transfer learned DCNN models on three datasets (SDNET, CCIC, BSD).
  • Applied two image enhancement techniques to improve crack detection on the SDNET dataset.
  • Extracted deep features from DCNNs to train Support Vector Machine (SVM) models.

Main Results:

  • ResNet101 achieved 53.40% accuracy on SDNET; EfficientNetB0 (98.8%) and ResNet50 (99.8%) excelled on BSD and CCIC, respectively.
  • Image enhancement significantly improved the accuracy of transfer-learned DCNN models on the SDNET dataset.
  • Integrating deep features with SVM enhanced detection accuracy across all DCNN-dataset combinations.

Conclusions:

  • Transfer learned DCNNs offer a robust approach to automated crack detection.
  • Image enhancement and feature extraction with SVM further boost detection performance.
  • This research provides valuable insights for improving infrastructure inspection efficiency and integrity.