用合成数据提升德国交通标志识别真实感与鲁棒性。
Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
- 结合生成模型与物理建模，合成带磨损和光照变化的交通标志图像。
- 含105500张图、211类标志，覆盖2020年新发布罕见标志。
- 支持可解释性AI与鲁棒性测试，适合自动驾驶系统训练验证。
本文提出一种合成流程与数据集，用于交通标志识别任务的训练与测试。该方法融合数据驱动与解析建模优势：基于GAN的纹理生成实现真实磨损与污渍效果，解析场景调制则确保光照物理准确性并支持参数精细控制。后者使模型对参数变化敏感性评估成为可能，适用于可解释人工智能（XAI）与鲁棒性测试，实验已验证其有效性。合成数据集Synset Signset Germany共包含105500张图像，覆盖211种德国交通标志类别，包括2020年新发布的较罕见标志。每张图像均附带掩码与分割图，并提供丰富的元数据，包括随机选择的环境与成像参数。我们在真实世界GTSRB基准与当前先进合成数据集CATERED上评估了该数据集的真实感表现。
原文摘要 · Abstract (English)
In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analytical modeling: GAN-based texture generation enables data-driven dirt and wear artifacts, rendering unique and realistic traffic sign surfaces, while the analytical scene modulation achieves physically correct lighting and allows detailed parameterization. In particular, the latter opens up applications in the context of explainable AI (XAI) and robustness tests due to the possibility of evaluating the sensitivity to parameter changes, which we demonstrate with experiments. Our resulting synthetic traffic sign recognition dataset Synset Signset Germany contains a total of 105500 images of 211 different German traffic sign classes, including newly published (2020) and thus comparatively rare traffic signs. In addition to a mask and a segmentation image, we also provide extensive metadata including the stochastically selected environment and imaging effect parameters for each image. We evaluate the degree of realism of Synset Signset Germany on the real-world German Traffic Sign Recognition Benchmark (GTSRB) and in comparison to CATERED, a state-of-the-art synthetic traffic sign recognition dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。