How to Detect and Decode QR Code with YOLO, OpenCV, and Dynamsoft Barcode Reader

In the past two weeks, I trained a custom YOLOv3 model for QR code detection and tested it with Darknet. In this article, I will use OpenCV’s DNN (Deep Neural Network) module to load the YOLO model for making detection from static images and real-time camera video stream. Besides, I will use Dynamsoft Barcode Reader to decode QR codes from the regions detected by YOLO.

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Combining Deep Learning and Computer Vision for Barcode Recognition

Last week, I trained a YOLOv3 model and a YOLOv3-tiny model to do barcode localization via deep learning. By comparing their performance, I dropped YOLOv3, because YOLOv3-tiny is much faster. I am satisfied with the QR code detection speed by running the YOLOv3-tiny model on my GeForce RTX2060 graphics card. In this article, I will power Darknet to decode QR code by integrating Dynamsoft C/C++ barcode SDK. My goal is to explore whether it is possible to utilize deep learning to boost barcode recognition performance.

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Darknet with CUDA: Train YOLO Model for QR Code Detection on Windows

In my previous article, I shared how to integrate Dynamsoft Barcode Reader to LabelImg for annotating barcode objects. It is time to take a further step to make some custom models for barcodes. In this article, I will go through the process that I used Darknet to train YOLO v3 models for QR code detection.

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