Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN

TL;DR

This paper introduces an integrated approach combining ESRGAN for image super-resolution and Faster R-CNN for object detection, significantly improving detection accuracy on low-resolution images.

Contribution

It presents a novel framework that enhances low-quality images before detection, outperforming traditional methods on low-resolution inputs.

Findings

Enhanced detection accuracy on low-resolution images

Superior performance compared to traditional methods

Effective in variable or limited image quality scenarios

Abstract

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