Abstract
As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.
Highlights
Image recognition is a technology that has been studied for a long time in the field of computer vision, as a technology for identifying information in an image
With the recent developments of artificial intelligence, deep learning-based image recognition is being applied in various industries, such as autonomous driving [1], medical diagnoses [2], facial recognition [3], and smart farms [4]
As part of the abovementioned research, the present study proposes a method for recognizing various types of line objects and flow arrows included in piping and instrumentation diagram (P&ID)
Summary
Image recognition is a technology that has been studied for a long time in the field of computer vision, as a technology for identifying information in an image. With the recent developments of artificial intelligence, deep learning-based image recognition is being applied in various industries, such as autonomous driving [1], medical diagnoses [2], facial recognition [3], and smart farms [4]. Various studies using deep learning have been conducted in the engineering field, such as drawing digitization [5], manufacturability verification [6], and fault diagnosis [7]. Piping and instrumentation diagrams (P&IDs) are developed based on process flow diagram (PFD) information. A P&ID includes piping, instrumentation, equipment, and fittings, which are components of each process, and the relationship between these components and the flow of fluids are depicted in detail in the diagram. When the need arises, the engineering information stored in P&IDs must be quickly and accurately searched
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