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:我们提出了一个数据驱动框架,将2D工程部分图纸的矢量化和机器解释自动化。在工业环境中,大多数制造工程师仍然依靠手动阅读,从设计师提交的图纸中确定地形学和制造要求。解释过程既费力又费时,严重抑制了部分引文和制造任务的效率。虽然基于图像的计算机视觉方法最近的进展显示了通过语义分解方法解释自然图像的巨大潜力,但在将工程技术图纸分解成语义准确组成部分时应用这些方法仍是一个重大挑战。工程图纸中的严重像素宽度也限制了基于图像的数据驱动方法的有效成型和制造要求。为了克服这些挑战,我们提出了一个深层次的学习框架,以预测每个矢量部分的语义类型和制造任务的效率。以光度图像成像作为投入,我们通过稀释、中风追踪和立方贝塞尔的装配对所有组成部分进行传导。然后根据各组成部分之间的连通性生成了这些组成部分的图表。最后,一个图表变色的内心网络也限制了基于图像驱动方法的图面结构结构结构结构图,我们每个部分的图纸结构结构图的图图式图,我们用图表图图式结构图图图图图图显示。