1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
Source abstract
Abstract
Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: QR Code Applications and Technologies · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science, National University of Defense Technology, Changsha 410000, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, China
References (21)
Show source-provided references
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