QR Code Detection YOLO

Data Usage and Goals

The primary objective is to train object detection models, such as YOLO (You Only Look Once), using synthetic QR codes and background images to improve generalization on unseen, real-world data. Training on the synthetic set avoids overfitting, while the test set evaluates the model’s performance in natural environments with QR codes placed in cluttered, busy scenes.

Data Augmentation and Variety

To make the dataset more robust, various augmentations are included:

• Diverse QR Code Sizes: Codes of varying sizes are use, enabling the model to detect small and large codes in the same setting.
• Rotations and Distortions: QR codes are display from multiple angles and subjected to distortions, mimicking real-life conditions where codes may not be perfectly align.
• Occlusion Handling: Some QR codes are partially obscured by objects, providing additional complexity for the detection task.

Potential Applications

This dataset is suit for AI developers working on:

• Mobile scanning apps
• Inventory management systems
• Augmented reality applications that rely on QR codes for object recognition.