随着物联网的发展,条形码技术在商业、医疗、运输和制造等领域都有不可或缺的作用。为了确保自动识别系统能够快速准确地读取条码信息,准确实时的条码定位算法是关键所在。首先着重介绍了条码定位算法研究:主要分为基于传统图像处理的...随着物联网的发展,条形码技术在商业、医疗、运输和制造等领域都有不可或缺的作用。为了确保自动识别系统能够快速准确地读取条码信息,准确实时的条码定位算法是关键所在。首先着重介绍了条码定位算法研究:主要分为基于传统图像处理的条形码定位方法和基于深度学习的条形码定位方法。传统图像处理方法通常包括图像预处理、边缘检测和模式匹配等步骤。这些方法在处理简单场景的条码定位时表现良好。深度学习方法通过构建卷积神经网络(CNN)等模型,实现了对图像特征的自动学习和提取,具有更好的适应性和泛化能力,其次详细介绍了条形码算法所用到的数据集,并且简单介绍条形码的发展和和评价指标,最后进行总结和展望。With the development of the Internet of Things (IoT), barcode technology plays an indispensable role in various fields including commerce, healthcare, transportation, and manufacturing. To ensure that automatic identification systems can quickly and accurately read barcode information, accurate and real-time barcode localization algorithms are crucial. This paper first provides an overview of barcode localization algorithm research, which is primarily divided into traditional image processing-based methods and deep learning-based methods. Traditional image processing methods typically include image preprocessing, edge detection, and pattern matching. These methods perform well for barcode localization in simple scenarios. Deep learning methods, through the construction of models such as Convolutional Neural Networks (CNNs), achieve automatic learning and extraction of image features, offering better adaptability and generalization capabilities. Furthermore, this paper details the datasets used in barcode algorithms, briefly introduces the development of barcodes and evaluation metrics, and concludes with a summary and outlook.展开更多
文摘随着物联网的发展,条形码技术在商业、医疗、运输和制造等领域都有不可或缺的作用。为了确保自动识别系统能够快速准确地读取条码信息,准确实时的条码定位算法是关键所在。首先着重介绍了条码定位算法研究:主要分为基于传统图像处理的条形码定位方法和基于深度学习的条形码定位方法。传统图像处理方法通常包括图像预处理、边缘检测和模式匹配等步骤。这些方法在处理简单场景的条码定位时表现良好。深度学习方法通过构建卷积神经网络(CNN)等模型,实现了对图像特征的自动学习和提取,具有更好的适应性和泛化能力,其次详细介绍了条形码算法所用到的数据集,并且简单介绍条形码的发展和和评价指标,最后进行总结和展望。With the development of the Internet of Things (IoT), barcode technology plays an indispensable role in various fields including commerce, healthcare, transportation, and manufacturing. To ensure that automatic identification systems can quickly and accurately read barcode information, accurate and real-time barcode localization algorithms are crucial. This paper first provides an overview of barcode localization algorithm research, which is primarily divided into traditional image processing-based methods and deep learning-based methods. Traditional image processing methods typically include image preprocessing, edge detection, and pattern matching. These methods perform well for barcode localization in simple scenarios. Deep learning methods, through the construction of models such as Convolutional Neural Networks (CNNs), achieve automatic learning and extraction of image features, offering better adaptability and generalization capabilities. Furthermore, this paper details the datasets used in barcode algorithms, briefly introduces the development of barcodes and evaluation metrics, and concludes with a summary and outlook.
