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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

96
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
96

You might also read

Related Articles

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

Sort by
Same author

Crop Pest Identification and Real-Time Monitoring System Design Based on Improved YOLOv8s.

Sensors (Basel, Switzerland)·2026
Same author

An Integrated Gather-and-Distribute Mechanism and Attention-Enhanced Deformable Convolution Model for Pig Behavior Recognition.

Animals : an open access journal from MDPI·2024
Same author

Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning.

Briefings in functional genomics·2023
Same author

SheepInst: A High-Performance Instance Segmentation of Sheep Images Based on Deep Learning.

Animals : an open access journal from MDPI·2023
Same author

SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data.

Briefings in functional genomics·2023
Same author

Immediate splenectomy down-regulates the MAPK-NF-κB signaling pathway in rat brain after severe traumatic brain injury.

The journal of trauma and acute care surgery·2013
Same journal

Correction: Gernhardt et al. Ex Vivo Computed Tomographic Morphometry and Motion of the Native and Fractured Equine Accessory Carpal Bone. <i>Animals</i> 2026, <i>16</i>, 1132.

Animals : an open access journal from MDPI·2026
Same journal

Camera-Trap Assessment of Terrestrial Mammals and Ground-Dwelling Birds in the Zhangjiajie Chinese Giant Salamander National Nature Reserve, China.

Animals : an open access journal from MDPI·2026
Same journal

Beyond the Mission: Long-Term Endocrine Dynamics in Search and Rescue Dog-Handler Teams.

Animals : an open access journal from MDPI·2026
Same journal

Phenotypic Characterisation of the Abruzzo Donkey (<i>Equus asinus</i>), an Endangered Italian Genetic Resource: Body Measurements.

Animals : an open access journal from MDPI·2026
Same journal

Assessment of Maternal Genetic Diversity and Mitochondrial Population Structure of Endangered Indigenous Chicken Breeds in China.

Animals : an open access journal from MDPI·2026
Same journal

Effects of Expected Progeny Difference and Feeding Systems on Carcass Characteristics in Hanwoo Steers.

Animals : an open access journal from MDPI·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals
12:18

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals

Published on: February 26, 2022

9.9K

A Real-Time Lightweight Behavior Recognition Model for Multiple Dairy Goats.

Xiaobo Wang1, Yufan Hu1, Meili Wang1,2

  • 1College of Information Engineering, Northwest A&F University, Yangling 712100, China.

Animals : an Open Access Journal From MDPI
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model, GSCW-YOLO, accurately detects dairy goat behaviors, including subtle and abnormal actions, using advanced feature recognition. This technology aids in early health issue detection and improves animal welfare management in modern farming.

Keywords:
GSCW-YOLOabnormal behaviorsbehavior recognitiondairy goatdeep learning

More Related Videos

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

5.9K
Noninvasive EEG Recordings from Freely Moving Piglets
04:05

Noninvasive EEG Recordings from Freely Moving Piglets

Published on: July 13, 2018

7.2K

Related Experiment Videos

Last Updated: Jun 3, 2025

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals
12:18

In Vivo Methods to Assess Retinal Ganglion Cell and Optic Nerve Function and Structure in Large Animals

Published on: February 26, 2022

9.9K
Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

5.9K
Noninvasive EEG Recordings from Freely Moving Piglets
04:05

Noninvasive EEG Recordings from Freely Moving Piglets

Published on: July 13, 2018

7.2K

Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Livestock behavior is a key indicator of animal health and welfare.
  • Automated behavior recognition using deep learning can enable early detection of health and environmental issues in dairy goats.
  • Recognizing small-target behaviors in complex farm environments presents significant challenges.

Purpose of the Study:

  • To develop a lightweight and multi-scale deep learning model for accurate dairy goat behavior recognition.
  • To enhance the detection of subtle, abnormal, and small-target behaviors in surveillance videos.
  • To provide a robust solution for intelligent management and welfare-focused breeding in the dairy goat industry.

Main Methods:

  • Proposed GSCW-YOLO, a novel model integrating Gaussian Context Transformation (GCT) and Content-Aware Reassembly of Features (CARAFE) upsampling.
  • Enhanced the YOLOv8n framework with a small-target detection layer and optimized Wise-IoU loss function.
  • Collected video data under varied lighting conditions and evaluated the model on a self-constructed dataset of 9213 images.

Main Results:

  • GSCW-YOLO achieved 93.5% precision, 94.1% recall, and 97.5% mAP, outperforming the baseline YOLOv8n.
  • Demonstrated significant improvements in detecting distant small-target and transient abnormal behaviors.
  • The model is highly efficient with a 5.9 MB size and 175 FPS, surpassing other popular models like CenterNet and EfficientDet.

Conclusions:

  • GSCW-YOLO offers superior performance for dairy goat behavior recognition, especially for challenging small-target and subtle actions.
  • The model provides effective technical support for intelligent dairy goat management and welfare.
  • This advancement contributes to the modernization and efficiency of the dairy goat industry.