Bar Code Detection for AI-Driven EDA and Big Data Analytics

This study employs deep learning techniques to detect barcodes in surveillance videos, with a focus on leveraging big data for promoting sustainability in urban environments. Our objective is to develop an automated system capable of swiftly and accurately identifying and categorizing barcodes. The proposed approach integrates data augmentation, transfer learning, convolutional neural networks (CNNs), and ResNet34 architecture. Annotated barcode images, categorized as ‘0’ for non-barcode and ‘1’ for barcode images, are utilized, with class datasets organized into separate folders. Preprocessing techniques, including image resizing and manipulation, facilitate relevant feature extraction by the model. Leveraging the Fastai library streamlines dataset handling, including importation, random splitting for training and validation, and data transformation. A data block is constructed to combine image and category information, enabling seamless processing. The ResNet34 architecture is employed to construct a convolutional neural network model, which undergoes transfer learning and fine-tuning specifically for barcode identification in the context of sustainable urban development. Learning rate selection, guided by analysis of the learning rate plot, optimizes model training. Evaluation is performed using error rate metrics, indicating a X% accuracy in barcode detection after two epochs of training with a learning rate of 1e-3. The proposed real-time surveillance approach significantly enhances barcode detection efficiency, with implications for supply chain logistics, retail, and inventory management. This work presents a robust deep learning-based algorithm for barcode detection in surveillance footage, with potential applications in creating automated barcode recognition systems for sustainable urban development. Future research directions include the deployment of real-time surveillance systems and addressing more complex barcode variants, thereby further advancing the field of barcode recognition technology within the framework of big data-driven sustainability initiatives for green cities.