• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.860-861
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• 2013

최근 고도의 산업 발전이 진행되면서 생산현장의 IT 기술을 접목한 자동화시스템이 대두되고 있다. 특히 설계 도면과 생산된 부품의 정합여부를 검사하여 제품의 생산 품질을 관리하는 도면정합검사는 기존의 수작업을 대체하고, 신뢰성을 보장할 수 있는 자동화 시스템의 도입이 필수적이다. 이에 본 논문에서는 비전 시스템을 기반으로 생산된 부품의 이미지를 인식/추출하고, 이를 이미지화된 도면과 매칭하여 정합 검사하는 도면정합검사시스템을 설계하였다. 따라서 기존의 수작업을 대체하여 생산의 효율을 높이고, 보다 정확한 검사기록을 관리하여 생산의 신뢰성 향상에 크게 기여할 수 있을 것이다.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.837-846
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• 2003

This paper deals with plain figures on the architectural drawings of apartment. This kind of architectural drawings consist of main walls represented by two parallel bold lines, symbols (door, window, $\cdots$), dimension line, extension line, and dimensions represent various numerical values and characters. This paper suggests a method for recognizing main wall which is a backbone of apartment in an architectural drawing. In this thesis, the following modules are realized : an efficient image barbarization, a removal of thin lines, a vectorization of detected lines, a region bounding for main walls, a calculation of extension lines, a finding main walls based on extension line, and a field expansion by searching other main walls which are linked with the detected main walls. Although the windows between main walls are not represented as main walls, a detection module for the windows is considered during the recognition period. So the windows are found as a part of main wall. An experimental result on 9 different architectural drawings shows 96.5% recognition of main walls and windows, which is about 5.8% higher than that of Karl Tombre.

• Convergence Security Journal
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• v.22 no.5
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• pp.3-9
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• 2022

Recently, in order to build a cyber physical system(CPS) that is a technology related to the 4th industry, the construction of the virtual control system for physical model and control circuit simulation is increasingly required in various industries. It takes a lot of time and money to convert documents that are not electronically documented through direct input. For this, it is very important to digitize a large number of drawings that have already been printed through object recognition using artificial intelligence. In this paper, in order to accurately recognize objects in drawings and to utilize them in various applications, a recognition technique using artificial intelligence by analyzing the characteristics of objects in drawing was proposed. In order to improve the performance of object recognition, each object was recognized and then an intermediate file storing the information was created. And the recognition rate of the next recognition target was improved by deleting the recognition result from the drawing. In addition, the recognition result was stored as a standardized format document so that it could be utilized in various fields of the control system. The excellent performance of the technique proposed in this paper was confirmed through the experiments.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.343-349
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• 2007

This paper addressed a recognition method of land registration map based on fuzzy inference scheme, which is able to solve the time complexity problem of typical method [Fig. 2]. Not only line color, thickness but also number, character are used as a fuzzy input parameter. It concentrated on generation of fuzzy association map, and useful informations are extracted result from fuzzy inference. These results are precedent process for estimating the construction space and restoring 3D automatic modeling. It can also utilize to the internet service acceleration propulsion business such as u-Gov based land registration service.

• Journal of the Korea Institute of Building Construction
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• v.20 no.3
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• pp.289-296
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• 2020

This study suggests a methodology for automatically extracting placing information from 2D reinforced concrete details drawings and generating a 3D reinforcement placing model to develop a mobile augmented reality for bar placing work. To make it easier for users to acquire placing information, it is suggested that users takes pictures of structural drawings using a camera built into a mobile device and extract placing information using vision recognition and the OCR(Optical Character Registration) tool. In addition, an augmented reality app is implemented using the game engine to allow users to automatically generate 3D reinforcement placing model and review the 3D models by superimposing them with real images. Details are described for application to the proposed methodology using the previously developed programming tools, and the results of implementing reinforcement augmented reality models for typical members at construction sites are reviewed. It is expected that the methodology presented as a result of application can be used for learning bar placing work or construction review.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.544-546
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• 2000

본 논문에서는 max-min 필터를 적용하는 방식의 도면 영상 열화와 강화에 따른 도면영상의 효과적인 전처리 방안과 벡터의 추출 및 건축 도면에서 나타나는 주벽의 특징에 기초한 주벽으로 결정하는 방안을 제시한다. 또한 영상의 획득시 발생할 수 있는 약간의 기울어짐(skew)에 영향받지 않는 벡터의 추출 및 병합방법을 통해 치수선과 치수선 끝점을 추출하고 인식하는 방법을 제시하고 있다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.45-45
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• 2004

B-rep 기반의 솔리드 복원 기법은 비교적 복잡한 물체의 경우에도 복원이 잘 되지만, 후보면의 수가 증가함에 따라 탐색 공간 및 시간이 기하급수적으로 늘어나는 단점이 있다. 빈번한 조합 탐색과 복잡한 기하 연산으로 인해 도면이 복잡해질수록 복원 효율성이 떨어지고, 모호성이 발생하는 문제가 있다. 그러나, 이차 곡면을 포함하는 복잡한 물체에 대해서도 복원이 가능하므로 복원 대상 범위가 넓다고 할 수 있다. CSG 기반의 솔리드 복원 기법은 세 투영면에서 돌출 시킨 각각의 솔리드를 서로 교차시켜서 3차원 물체를 복원하는 방법으로, 복잡한 조합 탐색이나 기하 연산 작업을 하지 않게 때문에 비교적 효율적인 복원이 가능하다.(중략)

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.05a
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• pp.87-91
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• 1998

본 연구에서는 통신망 관로도상 정보의 자동인식 및 사용자의 반자동 입력으로 전자 통신망 도면을 작성하고 통신망의 설비정보를 관리자에 의해 입력하여 데이터베이스화하고 통신망 설비의 관리에 이용할 수 있는 시스템을 개발한다. 이를 위해 먼저 손으로 작성된 관로도에서 버튼 업(botton up) 처리로 범례정보에 속하는 기호 정보와 선로 정보를 자동인식하고 기타정보는 사용자가 반자동 입력하여 이를 기초로 새로운 전자 통신망 관로도를 작성한다. 또한 통신망 설비의 관리에 기초가 되는 문자정보는 관리자의 요구에 따라 입력하여 통신망 설비의 관리에 사용할 수 있도록 데이터베이스화 만다. 본 방법에서는 통신망 관로도가 갖는 사전정의 충분한 이용과 기타 정보의 반자동 입력으로 도면의 복잡도에 관계없이 자동화된 도면을 얻을 수 있다. 또한 설비정보는 관리자에 의해 반자동으로 추가되어 관리되므로 통신망 설비를 용이하게 관리하는 기초가 된다. 시스템 구축을 통하여 그 결과를 고찰한다.