A Novel Framework for QR Code Detection and Decoding from Obscure Images using YOLO Object Detection and Real-ESRGAN Image Enhancement Technique

QR codes have become increasingly popular in various applications, such as inventory management, advertising, and payment systems. However, detecting and decoding QR codes from blurry or unclear images can be challenging due to various factors, such as low image resolution, noise. The research aims to propose an efficient approach for detecting and decoding QR codes from unclear images using the You Only Look Once (YOLO) object detection model and deep super-resolution techniques implemented through Real-ESRGAN [1]. Authors trained and evaluated five different YOLOv8 object detection models with varying sizes and parameters to detect and crop the QR code from the input image. Based on the evaluation results, authors selected the model which achieved the best performance in terms of precision and recall. The cropped QR code based on YOLOv8 bounding box, is then enhanced using real-ESRGAN [1], which applies deep super-resolution techniques to improve the quality of the image. Finally, the enhanced QR code is decoded using three QR code utility libraries in Python, namely pyzbar, zxing, and opencv, which are executed in parallel using multithreading for efficient retrieval in less time. The results after testing demonstrates the effectiveness of proposed approach in accurately detecting and decoding QR codes from blurry or unclear images. The proposed approach can be used in various fields, such as inventory management, advertising, and payment systems, to provide a seamless user experience.