Barcode and QR Code Object Detection: An Experimental Study on YOLOv8 Models

TL;DR

This study evaluates YOLOv8 models for barcode and QR code detection, demonstrating significant accuracy improvements through model scaling and fine-tuning on specialized datasets, advancing real-time object recognition capabilities.

Contribution

It provides an in-depth analysis of YOLOv8's performance on barcode and QR code detection, highlighting the impact of model scaling and dataset tuning on accuracy.

Findings

Nano model accuracy: 88.95%

Small model accuracy: 97.10%

Medium model accuracy: 94.10%

Abstract

This research work dives into an in-depth evaluation of the YOLOv8 (You Only Look Once) algorithm's efficiency in object detection, specially focusing on Barcode and QR code recognition. Utilizing the real-time detection abilities of YOLOv8, we performed a study aimed at enhancing its talent in swiftly and correctly figuring out objects. Through large training and high-quality-tuning on Kaggle datasets tailored for Barcode and QR code detection, our goal became to optimize YOLOv8's overall performance throughout numerous situations and environments. The look encompasses the assessment of YOLOv8 throughout special version iterations: Nano, Small, and Medium, with a meticulous attention on precision, recall, and F1 assessment metrics. The consequences exhibit large improvements in object detection accuracy with every subsequent model refinement. Specifically, we achieved an accuracy of 88.95% for the nano model, 97.10% for the small model, and 94.10% for the medium version, showcasing the incremental improvements finished via model scaling. Our findings highlight the big strides made through YOLOv8 in pushing the limits of computer vision, ensuring its function as a milestone within the subject of object detection. This study sheds light on how model scaling affects object recognition, increasing the concept of deep learning-based computer creative and prescient techniques.