The use of deep learning (DL) for barcode recognition and analysis has achieved remarkable success and has attracted great attention in various domains. Unlike other barcode recognition methods, DL-based approaches can significantly improve the speed and accuracy of both barcode detection and decoding. However, after almost a decade of progress, the current status of DL-based barcode recognition has yet to be thoroughly explored. Specifically, summaries of key insights and gaps remain unavailable in the literature. Therefore, this study aims to comprehensively review recent applications of DL methods in barcode recognition. We mainly conducted a well-constructed systematic literature review (SLR) approach to collect relevant articles and evaluate and summarize the state of the art. This study's contributions are threefold. First, the paper highlights new DL approaches' applicability to barcode localization and decoding processes and their potential to either reduce the time required or provide higher quality. Second, another main finding of this study signifies an increasing demand for public and specific barcode datasets that allow DL methods to learn more efficiently in the big data era. Finally, we conclude with a discussion on the crucial challenges of DL with respect to barcode recognition, incorporating promising directions for future research development.

Unmanned aerial vehicles (UAVs) have proven to be a key solution for nearly automated or smart warehouse operations, enabling receiving, picking, storage, and shipping processes to be timely and more efficient. However, there is a relative scarcity of review studies specifically on UAV-based warehouse management. Research knowledge and insights on UAV applications in this field are also limited and could not sufficiently or practically support decision-making on commercial utilization. To leverage the potential applications and current situation of UAVs, this study provides a systematic literature review (SLR) on UAV adoption in warehouse management. SLR approach was critically conducted to identify, select, assess, and summarize findings, mainly on the two descriptive research questions; what are the past applications of UAV, and what are critical factors affecting UAV adoption in warehouse management? Five key critical factors and 13 sub-factors could be observed. The results revealed that hardware (e.g., payloads, battery power, and sensors) and software factors (e.g., scheduling, path planning, localization, and navigation algorithms) are the most influential factors impacting drone adoption in warehouse management. The managerial implications of our research findings that guide decision-makers or practitioners to effectively employ UAV-based warehouse management in good practice are also discussed.

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Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model’s performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.