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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
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...
6.7K

You might also read

Related Articles

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

Sort by
Same author

AIM: An Advanced Hybrid Inference Model Combining Clinical Rules and Lifelog-Based Learning for Health Risk Prediction.

Life (Basel, Switzerland)·2026
Same author

StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.

Sensors (Basel, Switzerland)·2026
Same author

Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem.

Sensors (Basel, Switzerland)·2025
Same author

Effect of treadmill walking on cardiometabolic risk factors and liver function markers in older adults with MASLD: a randomized controlled trial.

BMC sports science, medicine & rehabilitation·2025
Same author

A Study on Staging Cystic Echinococcosis Using Machine Learning Methods.

Bioengineering (Basel, Switzerland)·2025
Same author

Versatile and Fast Electrochemical Activation Method for Carbon Nanotube Fibers with Diverse Active Materials.

Small methods·2024

Related Experiment Video

Updated: Aug 22, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
06:30

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

8.5K

StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information.

Seungwook Son1, Hanse Ahn1, Hwapyeong Baek1

  • 1Department of Computer Convergence Software, Korea University, Sejong 30019, Korea.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

This study introduces a deep learning method for accurate pig detection in farms, improving accuracy from 84% to 94% by using video analysis and generated background information.

Keywords:
backgrounddeep learningfacilityimage processingocclusionpig detectionstatic cameravideo monitoring

More Related Videos

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.6K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.6K

Related Experiment Videos

Last Updated: Aug 22, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
06:30

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

8.5K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.6K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.6K

Area of Science:

  • Agricultural technology
  • Computer vision
  • Deep learning

Background:

  • Accurate individual pig detection is crucial for farm management.
  • Deep learning has advanced single-image object detection.
  • Pig size variations and complex farm environments challenge current detection methods.

Purpose of the Study:

  • To develop a deep learning object detection method for accurate individual pig monitoring.
  • To address challenges posed by pig size differences and environmental complexity in commercial farms.

Main Methods:

  • Utilized video sequences from static cameras for pig detection.
  • Preprocessed images to standardize pig sizes.
  • Extracted and combined background and facility information into composite images for training.
  • Developed a deep-learning-based object detection approach.

Main Results:

  • Improved pig detection accuracy from 84% to 94%.
  • Demonstrated the effectiveness of using generated background and facility information.
  • Showcased the benefit of image preprocessing for size normalization.

Conclusions:

  • The proposed deep learning method significantly enhances individual pig detection accuracy in complex farm settings.
  • Generated background and facility information from video sequences is effective for improving detection.
  • Future work should focus on improving detection of overlapping pigs.