The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.

翻译：工程图纸的数字化对于其高效复用、分发与存档至关重要。现有的工程图纸数字化计算机视觉方法通常假设输入图纸具有高质量。然而实际中，工程图纸常因不当的扫描、存储与传输而模糊或畸变，这可能削弱现有方法的有效性。本文聚焦于低质量工程图纸的复原与识别，提出一种端到端框架，旨在提升图纸质量并识别其中的图形符号。该框架利用K-means聚类，基于灰度共生矩阵统计特征将不同工程图纸子块分为简单纹理与复杂纹理两类。分别采用计算机视觉操作与改进型增强超分辨率生成对抗网络（ESRGAN）模型提升两类子块的质量。采用改进型基于区域的快速卷积神经网络（Faster R-CNN）模型识别质量增强后的图形符号。此外，提出一种多阶段任务驱动协同学习策略，训练改进型ESRGAN与Faster R-CNN模型，使其沿有利于图形符号识别（而非人类视觉感知）的方向提升工程图纸分辨率。还提出一种合成数据生成方法，用于构建训练框架所需的质量退化样本。基于真实电气接线图的实验表明，所提框架达到98.98%的准确率与99.33%的召回率，展现其相较于先前方法的优越性。此外，该框架已集成至一款广泛应用的电力系统软件中，验证了其实用性。