Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis
This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/6/1060 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850207716190978048 |
|---|---|
| author | Enrique Heredia-Aguado Juan José Cabrera Luis Miguel Jiménez David Valiente Arturo Gil |
| author_facet | Enrique Heredia-Aguado Juan José Cabrera Luis Miguel Jiménez David Valiente Arturo Gil |
| author_sort | Enrique Heredia-Aguado |
| collection | DOAJ |
| description | This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited by lighting conditions. This limitation poses a significant challenge in image detection playing a larger role in everyday technology, where illumination cannot always be controlled. Far-infrared images (which are partially invariant to lighting conditions) can serve as a valuable complement to RGB images in environments where illumination cannot be controlled and robust object detection is needed. In this work, various early and middle fusion techniques are presented and compared using different multispectral datasets, with the aim of addressing these limitations and improving detection performance. |
| format | Article |
| id | doaj-art-c408803f4dfa4827b2c1eb9eb841dc91 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c408803f4dfa4827b2c1eb9eb841dc912025-08-20T02:10:24ZengMDPI AGRemote Sensing2072-42922025-03-01176106010.3390/rs17061060Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance AnalysisEnrique Heredia-Aguado0Juan José Cabrera1Luis Miguel Jiménez2David Valiente3Arturo Gil4University Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche, Alicante, SpainUniversity Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche, Alicante, SpainUniversity Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche, Alicante, SpainUniversity Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche, Alicante, SpainUniversity Institute for Engineering Research, Miguel Hernández University, Avda. de la Universidad s/n, 03202 Elche, Alicante, SpainThis paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited by lighting conditions. This limitation poses a significant challenge in image detection playing a larger role in everyday technology, where illumination cannot always be controlled. Far-infrared images (which are partially invariant to lighting conditions) can serve as a valuable complement to RGB images in environments where illumination cannot be controlled and robust object detection is needed. In this work, various early and middle fusion techniques are presented and compared using different multispectral datasets, with the aim of addressing these limitations and improving detection performance.https://www.mdpi.com/2072-4292/17/6/1060thermal imagesperson detectionmultispectral image fusiondeep learningcomputer vision |
| spellingShingle | Enrique Heredia-Aguado Juan José Cabrera Luis Miguel Jiménez David Valiente Arturo Gil Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis Remote Sensing thermal images person detection multispectral image fusion deep learning computer vision |
| title | Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis |
| title_full | Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis |
| title_fullStr | Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis |
| title_full_unstemmed | Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis |
| title_short | Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis |
| title_sort | static early fusion techniques for visible and thermal images to enhance convolutional neural network detection a performance analysis |
| topic | thermal images person detection multispectral image fusion deep learning computer vision |
| url | https://www.mdpi.com/2072-4292/17/6/1060 |
| work_keys_str_mv | AT enriqueherediaaguado staticearlyfusiontechniquesforvisibleandthermalimagestoenhanceconvolutionalneuralnetworkdetectionaperformanceanalysis AT juanjosecabrera staticearlyfusiontechniquesforvisibleandthermalimagestoenhanceconvolutionalneuralnetworkdetectionaperformanceanalysis AT luismigueljimenez staticearlyfusiontechniquesforvisibleandthermalimagestoenhanceconvolutionalneuralnetworkdetectionaperformanceanalysis AT davidvaliente staticearlyfusiontechniquesforvisibleandthermalimagestoenhanceconvolutionalneuralnetworkdetectionaperformanceanalysis AT arturogil staticearlyfusiontechniquesforvisibleandthermalimagestoenhanceconvolutionalneuralnetworkdetectionaperformanceanalysis |