论文标题
通过知识图和机器学习加速道路标志地面真相构造
Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning
论文作者
论文摘要
拥有全面，高质量的路标注释数据集对于基于AI的路标识别（RSR）系统的成功至关重要。实际上，注释者通常在学习不同国家的道路标志系统方面面临困难。因此，这些任务通常是耗时的，并产生差的结果。我们提出了一种使用知识图和机器学习算法 - 变分原型编码器（VPE）的新方法，以帮助人类注释有效地对路标进行分类。注释者可以使用视觉属性查询路标知识图，并接收VPE模型建议的最接近的匹配候选人。 VPE模型使用知识图中的候选者，将真实的符号图像补丁作为输入。我们表明，我们的知识图方法可以将标志搜索空间减少98.9％。此外，使用VPE，我们的系统可以为测试数据集中75％的符号提出正确的候选者，从而完全消除了人类的搜索工作。
Having a comprehensive, high-quality dataset of road sign annotation is critical to the success of AI-based Road Sign Recognition (RSR) systems. In practice, annotators often face difficulties in learning road sign systems of different countries; hence, the tasks are often time-consuming and produce poor results. We propose a novel approach using knowledge graphs and a machine learning algorithm - variational prototyping-encoder (VPE) - to assist human annotators in classifying road signs effectively. Annotators can query the Road Sign Knowledge Graph using visual attributes and receive closest matching candidates suggested by the VPE model. The VPE model uses the candidates from the knowledge graph and a real sign image patch as inputs. We show that our knowledge graph approach can reduce sign search space by 98.9%. Furthermore, with VPE, our system can propose the correct single candidate for 75% of signs in the tested datasets, eliminating the human search effort entirely in those cases.
