One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models. To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data-structure for capturing the structure of the piping schematics. We evaluated proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained promising results.
There exists very limited work on digitizing the content of engineering diagrams to facilitate fast and efficient extraction of information. The authors @cite_14 automated the assessment of AutoCAD Drawing Exchange Format (DXF) by converting DXF file into SVG format and developing a marking algorithm of the generated SVG files. A framework for engineering drawings recognition using a case-based approach is proposed by @cite_10 where the user interactively provides an example of one type of graphic object in an engineering drawing and then system tries to learn the graphical knowledge of this type of graphic object from the example and later use this learned knowledge to recognize or search for similar graphic objects in engineering drawings. Authors of @cite_6 tried to automate the extraction of structural and connectivity information from vector-graphics-coded engineering documents. A spatial relation graph (SRG) and its partial matching method are proposed for online composite graphics representation and recognition in @cite_3 . Overall, we observed that there does not exist much work on information extraction from plant engineering diagrams.
@cite_3 @cite_14 @cite_10 @cite_6
1583110160 2107266793 2210190747 2031948510
In this paper, we propose a framework for engineering drawings recognition using a case-based approach. The key idea of our scheme is that, interactively, the user provides an example of one type of graphic object in an engineering drawing, then the system learns the graphical knowledge of this type of graphic object from the example and uses this learned knowledge to recognize or search for similar graphic objects in engineering drawings. The scheme emphasizes the following three distinct characteristics: automatism, run-time-ness, and robustness. We summarized five types of geometric constraints to represent the generic graphical knowledge. We also developed two algorithms for case-based graphical knowledge acquisition and knowledge-based graphics recognition, respectively. Experiments have shown that our proposed framework is both efficient and effective for recognizing various types of graphic objects in engineering drawings. A spatial relation graph (SRG) and its partial matching method are proposed for online composite graphics representation and recognition. The SRG-based approach emphasizes three characteristics of online graphics recognition: partial, structural, and independent of stroke order and stroke number. A constrained partial permutation strategy is also proposed to reduce the computational cost of matching two SRGs, which is originally an NP-complete problem as is graph isomorphism. Experimental results show that our proposed SRG-based approach is both efficient and effective for online composite graphics recognition in our sketch-based graphics input system - SmartSketchpad. Assessment of student's Engineering Drawing (ED) is always tedious, repetitive and time consuming. Image processing has been the common method to convert ED to be automatically assessed. This method is tedious as algorithms need to be developed for each shape to be assessed. Our research aims to create a software application that is able to perform automatic assessment for AutoCAD Drawing Exchange Format (DXF) files for undergraduate ED course. To achieve this goal, we have explored methods to convert DXF files into SVG format and develop a marking algorithm for the generated SVG files. The result shows that it is feasible to create software that automatically assesses ED without human intervention. Future implementation would include complex real-world ED. We present our recent model of a diagram recognition engine. It extends our previous work which approaches the structural recognition as an optimization problem of choosing the best subset of symbol candidates. The main improvement is the integration of our own text separator into the pipeline to deal with text blocks occurring in diagrams. Second improvement is splitting the symbol candidates detection into two stages: uniform symbols detection and arrows detection. Text recognition is left for post processing when the diagram structure is already known. Training and testing of the engine was done on a freely available benchmark database of flowcharts. We correctly segmented and recognized 93.0 of the symbols having 55.1 of the diagrams recognized without any error. Considering correct stroke labeling, we achieved the precision of 95.7 . This result is superior to the state-of-the-art method with the precision of 92.4 . Additionally, we demonstrate the generality of the proposed method by adapting the system to finite automata domain and evaluating it on own database of such diagrams.