Fiducial markers are a computer vision tool used for object pose estimation and detection. These markers are highly useful in fields such as industry, medicine and logistics. However, optimal lighting conditions are not always available,and other factors such as blur or sensor noise can affect image quality. Classical computer vision techniques that precisely locate and decode fiducial markers often fail under difficult illumination conditions (e.g. extreme variations of lighting within the same frame). Hence, we propose DeepArUco++, a deep learning-based framework that leverages the robustness of Convolutional Neural Networks to perform marker detection and decoding in challenging lighting conditions. The framework is based on a pipeline using different Neural Network models at each step, namely marker detection, corner refinement and marker decoding. Additionally, we propose a simple method for generating synthetic data for training the different models that compose the proposed pipeline, and we present a second, real-life dataset of ArUco markers in challenging lighting conditions used to evaluate our system. The developed method outperforms other state-of-the-art methods in such tasks and remains competitive even when testing on the datasets used to develop those methods. Code available in GitHub: https://github.com/AVAuco/deeparuco/

翻译：基准标记是一种用于物体姿态估计与检测的计算机视觉工具。这些标记在工业、医学和物流等领域极具实用价值。然而，理想的光照条件并非总能获得，且模糊或传感器噪声等其他因素也会影响图像质量。传统计算机视觉技术虽能精确定位和解码基准标记，但在困难光照条件下（例如同一帧内光照剧烈变化）常常失效。为此，我们提出DeepArUco++，这是一个基于深度学习的框架，利用卷积神经网络（CNN）的鲁棒性，在挑战性光照条件下执行标记检测与解码。该框架基于分步处理流程，每一步采用不同的神经网络模型，即标记检测、角点优化和标记解码。此外，我们提出一种生成合成数据的简易方法，用于训练构成该流程的各个模型，并提供了第二个真实场景下的ArUco标记数据集（包含挑战性光照条件），用于评估我们的系统。所开发的方法在此类任务中优于其他先进方法，即使在用于开发这些方法的原始数据集上进行测试，仍保持竞争力。代码发布于GitHub：https://github.com/AVAuco/deeparuco/