Data Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.

作为被动视觉地标印记的数据矩阵模式已被证明是车间自动导引车 (AGV) 自我定位的有效解决方案。然而，现有的数据矩阵解码应用程序需要很长时间来检测和分割输入图像中的标记。因此，本文提出了一种pipeline，其中检测器基于实时深度学习网络，解码器是常规方法，即libdmtx中的实现. 为此，研究、训练、比较和评估了几种用于对象检测的深度神经网络 (DNN)。架构范围从区域提议（Faster R-CNN）到单次方法（SSD 和 YOLO）。本研究侧重于性能和处理时间，以选择最佳的深度学习 (DL) 模型来进行视觉标记的检测。此外，还创建了一个特定的数据集来评估这些网络。该测试集包括苛刻的情况，例如同一场景中的高光照梯度和位于倾斜平面中的数据矩阵标记。所提出的方法优于libdmtx 等库中最著名和最常用的数据矩阵解码器。