Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

You might also read

Related Articles

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

Sort by

Same author

Detection of unmanned aerial vehicles using a visible camera system.

Applied optics·2017

Same author

Effects of surface materials on polarimetric-thermal measurements: applications to face recognition.

Applied optics·2016

Same author

Temporal pattern of acoustic imaging noise asymmetrically modulates activation in the auditory cortex.

Hearing research·2015

Same author

Thermal-to-visible face recognition using partial least squares.

Journal of the Optical Society of America. A, Optics, image science, and vision·2015

Same author

Improving cross-modal face recognition using polarimetric imaging.

Optics letters·2015

Same author

Face recognition performance with superresolution.

Applied optics·2012

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Published on: November 6, 2015

Long-Range Thermal Target Detection in Data-Limited Settings Using Restricted Receptive Fields.

Domenick Poster¹, Shuowen Hu², Nasser M Nasrabadi¹

¹Lane Department of Computer Science and Electrical Engineering, West Virginia University, 395 Evansdale Dr., Morgantown, WV 26506, USA.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

This study introduces a new convolutional neural network (CNN) for detecting small objects in thermal images, especially in low-data scenarios. The method improves detection accuracy by focusing on specific image features, outperforming existing techniques.

Keywords:

automated target recognition deep learning small object detection thermal infrared

More Related Videos

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Related Experiment Videos

Last Updated: Jul 15, 2025

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Published on: November 6, 2015

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Long-range target detection in thermal infrared imagery is difficult due to low resolution and limited data.
Small object detection algorithms struggle with small, variable thermal image datasets.

Purpose of the Study:

To propose a novel convolutional neural network (CNN) feature extraction architecture for small object detection in data-limited thermal imagery.
To address sensor and data constraints in thermal imaging for improved target detection.

Main Methods:

Developed a CNN architecture with restricted receptive fields, reduced downsampling, and attenuated fine-grained feature processing.
Inspired by popular object detectors and custom-designed networks for feature extraction.
Evaluated the algorithm on ground-based, drone, and satellite aerial imagery.

Main Results:

Achieved greatly improved detection rates by focusing on restricted receptive fields and specific feature processing.
Mitigated model overfitting on small or poorly varied datasets.
Reached state-of-the-art results on the DSIAC ATR and AI-TOD datasets.

Conclusions:

The proposed CNN architecture effectively enhances small object detection in thermal imagery, even with limited data.
The approach demonstrates versatility across different imaging platforms (ground, drone, satellite).
The findings suggest a promising direction for robust automated target recognition in challenging thermal imaging conditions.