Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915340603097088 |
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| author | K, Divya Swetha Choudhury, Ziaul Haque Bhuyan, Hemanta Kumar Brahma, Biswajit Kamila, Nilayam Kumar |
| author_facet | K, Divya Swetha Choudhury, Ziaul Haque Bhuyan, Hemanta Kumar Brahma, Biswajit Kamila, Nilayam Kumar |
| contents | In this study, proposes a method for improved object detection from the low-resolution images by integrating Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) and Faster Region-Convolutional Neural Network (Faster R-CNN). ESRGAN enhances low-quality images, restoring details and improving clarity, while Faster R-CNN performs accurate object detection on the enhanced images. The combination of these techniques ensures better detection performance, even with poor-quality inputs, offering an effective solution for applications where image resolution is in consistent. ESRGAN is employed as a pre-processing step to enhance the low-resolution input image, effectively restoring lost details and improving overall image quality. Subsequently, the enhanced image is fed into the Faster R-CNN model for accurate object detection and localization. Experimental results demonstrate that this integrated approach yields superior performance compared to traditional methods applied directly to low-resolution images. The proposed framework provides a promising solution for applications where image quality is variable or limited, enabling more robust and reliable object detection in challenging scenarios. It achieves a balance between improved image quality and efficient object detection |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11122 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN K, Divya Swetha Choudhury, Ziaul Haque Bhuyan, Hemanta Kumar Brahma, Biswajit Kamila, Nilayam Kumar Computer Vision and Pattern Recognition In this study, proposes a method for improved object detection from the low-resolution images by integrating Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) and Faster Region-Convolutional Neural Network (Faster R-CNN). ESRGAN enhances low-quality images, restoring details and improving clarity, while Faster R-CNN performs accurate object detection on the enhanced images. The combination of these techniques ensures better detection performance, even with poor-quality inputs, offering an effective solution for applications where image resolution is in consistent. ESRGAN is employed as a pre-processing step to enhance the low-resolution input image, effectively restoring lost details and improving overall image quality. Subsequently, the enhanced image is fed into the Faster R-CNN model for accurate object detection and localization. Experimental results demonstrate that this integrated approach yields superior performance compared to traditional methods applied directly to low-resolution images. The proposed framework provides a promising solution for applications where image quality is variable or limited, enabling more robust and reliable object detection in challenging scenarios. It achieves a balance between improved image quality and efficient object detection |
| title | Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.11122 |