2025/06/10 by Rogina J. S. Savarimuthu, K, Divya Swetha, Ziaul Haque Choudhury +7
Computer Science · Engineering · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection
paper · pdf · doi:10.48550/arxiv.2506.11122
openalex publication_date 2025/06/10 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
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