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Abstract：
The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.
摘要：
数据矩阵(DM)码的识别在工业生产中起着至关重要的作用。现有方法已取得重大进展。然而,对于在工业生产环境中DM代码的L形实心边缘（取景器图案）和虚线边缘（时序图案）上具有突起和中断的低质量图像，由于缺乏对这些干扰问题的考虑，现有方法的识别准确率急剧下降。因此,在存在这些干扰问题的情况下确保识别准确性是一项极具挑战性的任务。为了解决这些干扰问题，与大多数专注于定位L形固体边缘以进行DM代码识别的现有方法不同，本文提出了一种新的DM码识别方法，该方法通过结合DM码中心的先验信息来定位L形虚线边缘。具体来说,我们首先使用基于深度学习的对象检测方法来获取DM代码的中心。接下来,为了提高L形虚线边缘定位的精度，我们设计了一个结合一般约束和中央约束的两级筛查策略.中央约束充分利用DM码中心的先验信息。最后,我们使用libdmtx从DM代码的精确位置图像中解码内容。通过使用L形虚线边缘生成图像。在各种类型的DM代码数据集上的实验结果表明，所提出的方法在识别准确率和时间消耗方面都优于比较的方法。因此在工业生产环境中具有重要的实用价值。
