用超分增强低清图像，提升目标检测精度
Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN
- 先用ESRGAN提升图像分辨率，再用Faster R-CNN检测
- 在低分辨率图像上检测准确率显著优于传统方法
- 适合图像质量差或不稳定的实时检测场景
本研究提出一种融合增强型超分辨率生成对抗网络（ESRGAN）与更快区域卷积神经网络（Faster R-CNN）的物体检测方法，用于提升低分辨率图像中的检测性能。ESRGAN作为预处理模块，恢复图像细节并提高清晰度；随后将增强后的图像输入Faster R-CNN进行精确的目标检测与定位。实验表明，该集成方法在低分辨率输入下表现优于直接使用传统方法的对比方案。该框架在图像质量不稳定或受限的应用中展现出更强的鲁棒性，实现了图像质量提升与高效检测之间的良好平衡。
原文摘要 · Abstract (English)
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
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