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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | Object detection for low-resolution image using GAN-Yolo-4 for UAV aerial images data-set |
| Authors Name: | Vikul Kumar , Kajal Kori |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_201438
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| Published Paper Id: | IJRTI2503123 |
| Published In: | Volume 10 Issue 3, March-2025 |
| DOI: | |
| Abstract: | Aerial photography object identification by un- manned aerial vehicles (UAVs) is crucial for disaster relief, environmental protection and military monitoring. Broad photog- raphy, which adds background noise, and high imaging height, which makes it more likely that there may be little things in the images, are the problems. In order to get around this problem, re- mote sensing has resorted to super-resolution Generative Adver- sarial Networks (GANs), specifically enhanced super-resolution GANs (ESRGAN) and EESRGAN, which excel in spotting small objects in fuzzy images. Due to this accomplishment, we suggest employing comparable ESRGAN approaches to improve photos of road surfaces. We want to enhance the image quality of remote sensing images using residual-in-residual dense blocks (RRDB). We develop edge-enhanced super-resolution GAN (EESRGAN), using edge enhancement methods, to further improve the pictures and identify small objects. We use different detector networks, such as Yolo-4, SSD and FRCNN in an end-to-end method, which is essential for reliably recognizing object. The performance of detection is improved by backpropagating the detector loss into EESRGAN. We thoroughly test our methods using datasets such as COWC, CIFAR-10, and COCO. |
| Keywords: | You Only Look Once (Yolo-4 v4),GAN, low- resolution (LR) image classification, single-shot multibox detector (SSD), faster region-based convolutional neural network (FR- CNN), edge improvement, satellite imagery, and high-resolution. |
| Cite Article: | "Object detection for low-resolution image using GAN-Yolo-4 for UAV aerial images data-set", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b151-b161, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503123.pdf |
| Downloads: | 000539 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2503123
Registration ID:201438
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier):
Page No: b151-b161 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503123 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503123 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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