Jove
Visualize
Contact Us

Related Concept Videos

Detection of Black Holes01:10

Detection of Black Holes

2.5K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
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...
8.0K
Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

1.0K
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
1.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
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...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.8K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Oligosaccharide prebiotics in functional foods and therapeutics: innovations and challenges.

3 Biotech·2026
Same author

Optimized car parts detection with advanced feature fusion and attention modules.

Scientific reports·2025
Same author

Multiple kidney stones prediction with efficient RT-DETR model.

Computers in biology and medicine·2025
Same author

A reliable anchor regenerative-based transformer model for x-small and dense objects recognition.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Preparation of cellulose composites with in situ generated copper nanoparticles using leaf extract and their properties.

Carbohydrate polymers·2016
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 8, 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

991

An efficient YOLOv12-based framework for detecting extremely small-scale objects.

A Chandrashekhar1, B Satyanarayana2, Rajani Reddy Gorrepati3

  • 1Department of Mechanical Engineering, Faculty of Science and Technology, Icfai Foundation for Higher Education, Hyderabad, 501203, Telangana, India.

Scientific Reports
|December 12, 2025
PubMed
Summary

This study introduces an efficient YOLOv12 model for detecting small objects in aerial imagery from drones. The novel Area-Attention C2f (A2C2F) module and Cross Stage Partial with Kernel size 2 (C3K2) module enhance feature learning and reduce computational cost for robust detection.

Keywords:
Area-Attention C2fC3K2Decoupled detection headExtremely small object detectionLightweight attention moduleMulti-scale feature fusion

More Related Videos

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

9.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Related Experiment Videos

Last Updated: Jan 8, 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

991
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

9.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Object detection in aerial imagery from UAVs faces challenges with small and extremely small objects.
  • Existing YOLO variants struggle with detecting minuscule objects in drone-captured data.

Purpose of the Study:

  • To propose an efficient YOLOv12 model for enhanced small and extremely small object detection in aerial imagery.
  • To introduce novel modules and strategies for improved feature learning and detection accuracy.

Main Methods:

  • Developed the Area-Attention C2f (A2C2F) module fusing multi-head MLP with localized area-attention.
  • Integrated the lightweight Cross Stage Partial with Kernel size 2 (C3K2) module to reduce computational complexity.
  • Implemented a multi-scale fusion strategy with A2C2F blocks for high-resolution feature preservation.
  • Designed a novel detection head with decoupled classification and regression branches and attention-guided feature fusion.

Main Results:

  • The proposed YOLOv12 model effectively detects objects smaller than 3-5 pixels, outperforming traditional YOLO variants.
  • Achieved Precision: 69.1%, Recall: 48.5%, F1-score: 56.99%, mAP@50: 58.8%, and mAP@0.5:0.95: 40.9% on the VisDrone dataset.
  • Demonstrated efficient inference at ~40 FPS on an A100 GPU with 59.1M parameters and 198.6 GFLOPs.
  • Outperformed two-stage and anchor-based models at a lower input resolution (640x640).

Conclusions:

  • The proposed YOLOv12 framework offers a robust and computationally efficient solution for small and extremely small object detection in UAV-based aerial imagery.
  • The novel A2C2F and C3K2 modules, along with the multi-scale fusion and detection head, significantly improve detection performance and inference speed.
  • The model is suitable for real-time UAV edge deployment due to its efficiency and high accuracy in challenging detection scenarios.