<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we proposed a comparative research project on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-19, ResNet-50, Inception-V3, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above-mentioned pre-trained models of CNN and R-CNN to achieve optimal results for accuracy as well as productivity in detection of various objects such as ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, a convolutional neural network (CNN) is used as the base model. For complex computations and for speeding up results, transfer learning is used. With the help of experimental analysis, we have discovered that R-CNN and Inception-V3 performed best out of the five pre-trained models.</p>
</abstract>

In this paper, we proposed a comparative research project on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-19, ResNet-50, Inception-V3, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above-mentioned pre-trained models of CNN and R-CNN to achieve optimal results for accuracy as well as productivity in detection of various objects such as ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, a convolutional neural network (CNN) is used as the base model. For complex computations and for speeding up results, transfer learning is used. With the help of experimental analysis, we have discovered that R-CNN and Inception-V3 performed best out of the five pre-trained models.

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on Dota

College of Computing Sciences & Information Technology

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, we proposed a comparative research project on the classification of various objects in satellite images using some pre-trained models of CNN...

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on Dota

Online veröffentlicht: 23. Juni 2024

Seitenbereich: 31 - 45

Eingereicht: 24. Apr. 2023

Akzeptiert: 20. Sept. 2023

DOI: https://doi.org/10.14313/jamris/2-2024/11

Schlüsselwörter
remote sensing images, CNN, R-CNN, transfer learning, object detection

© 2024 Hina Hashmi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on Dota

Online veröffentlicht: 23. Juni 2024

Seitenbereich: 31 - 45

Eingereicht: 24. Apr. 2023

Akzeptiert: 20. Sept. 2023

DOI: https://doi.org/10.14313/jamris/2-2024/11

Schlüsselwörterremote sensing images, CNN, R-CNN, transfer learning, object detection

© 2024 Hina Hashmi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
remote sensing images, CNN, R-CNN, transfer learning, object detection