Deep Learning for Industrial Barcode Recognition at High Throughput
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P108Keywords:
Deep Learning, YOLO, Convolutional Neural Networks (CNN), Barcode Recognition, High Throughput, Industrial Automation, Image Processing, Edge ComputingAbstract
In modern industrial settings, barcode and QR code recognition play a critical role in automation, inventory tracking, and production line management. High-throughput environments demand fast, accurate and robust recognition systems that can function reliably under varying lighting conditions, orientations, and printing inconsistencies. This paper explores the use of Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) object detection models for high-throughput industrial barcode recognition. The study highlights benchmark results under diverse environmental parameters such as printing speeds, motion blur, and illumination intensity. We propose a hybrid deep learning architecture that combines the localization efficiency of YOLO with the classification strength of CNNs to optimize both detection speed and recognition accuracy. The dataset consists of synthetically augmented and real-world barcode and QR code images collected from various production environments. Our model is trained using TensorFlow and PyTorch, optimized with advanced loss functions such as focal loss and IoU loss, and benchmarked against traditional OCR and classical computer vision techniques. The results demonstrate a notable improvement in recognition accuracy, throughput rate, and robustness to environmental variability. Our methodology includes data preprocessing techniques such as histogram equalization and affine transformations, combined with training strategies like transfer learning and data augmentation. We achieve a top-1 accuracy of 98.4% and a mean Average Precision (mAP) of 96.2% on our test dataset, even under challenging real-time industrial constraints. This paper also discusses the integration challenges of deep learning systems in legacy manufacturing ecosystems and presents a modular deployment strategy using edge computing devices and IoT gateways. The implications of our research extend to automated logistics, real-time quality inspection, and industrial IoT (IIoT) systems. Future work will focus on improving interpretability, reducing computational load, and extending the system to multilingual and distorted code environments
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