We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.

翻译：我们提出了一种数据驱动的框架，用于自动化二维工程零件图纸的矢量化与机器解释。在工业环境中，大多数制造工程师仍依赖人工阅读来识别设计师提交图纸中的拓扑和制造要求。这一解释过程费时费力，严重制约了零件报价与制造任务的效率。尽管基于图像的计算机视觉方法近期在通过语义分割解释自然图像方面展现出巨大潜力，但将这些方法应用于解析工程图纸中的语义准确组件仍面临重大挑战。工程图纸中像素的极端稀疏性也限制了基于图像的数据驱动方法的有效特征化。为克服这些难题，我们提出了一种基于深度学习的框架，用于预测每个矢量化组件的语义类型。该框架以光栅图像为输入，通过细化、笔画追踪和三次贝塞尔拟合实现所有组件的矢量化，随后基于组件之间的连接性生成组件图，最后在此图数据上训练图卷积神经网络以识别每个组件的语义类型。我们在工程图纸中文本、尺寸和轮廓组件的语义分割场景下测试了该框架。结果表明，与近期基于图像和图的现有分割方法相比，本方法取得了最优性能。