A Study on the Construction Equipment Object Extraction Model Based on Computer Vision Technology

doi:10.15683/kosdi.2023.12.31.916

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2023 Vol.19, Issue 4 Preview Page Next Page

Original Article

A Study on the Construction Equipment Object Extraction Model Based on Computer Vision Technology 컴퓨터 비전 기술 기반 건설장비 객체 추출 모델 적용 분석 연구: 강성원¹ㆍ유위성²ㆍ신윤석³*
Sungwon Kang¹, Wisung Yoo², Yoonseok Shin³*; ¹Architectural Engineer, Construction Team, Dongbu Corporation, Seoul, Republic of Korea
²Research Fellow, Department of Economic and Financial Research, Construction & Economy Research Institute of Korea, Seoul, Republic of Korea
³Professor, Department of Architectural Engineering, Kyonggi university, Suwon, Republic of Korea

31 December 2023. pp. 916-923

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Abstract

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site ‘A’, objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site ‘B’ showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

Keywords

Computer Vision

YOLACT

Construction Equipment

Object Extraction

Safety Management

연구목적: 2022년 산업재해 현황 부가통계에서 건설업 사망사고자 현황을 보면 건설업 전체 사망사고자의 27.8%가 건설장비로 인해 발생하고 있다. 현장 대형화, 고층화 등으로 발생하는 순회 및 점검의 한계를 극복하기 위해 컴퓨터 비전 기술을 활용해 건설장비를 추출할 수 있는 모델을 구축하고 해당 모델의 정확도 및 현장 적용성에 대해 분석하고자 한다. 연구방법: 본 연구에서는 건설장비 중 굴착기, 덤프트럭, 이동식 크레인의 이미지 데이터를 딥러닝 학습시킨 뒤 학습 결과를 평가 및 분석하고 건설현장에 적용하여 분석한다. 연구결과: ‘A’ 현장에서는 굴착기 및 덤프트럭의 객체를 추출하였으며, 평균 추출 정확도는 굴착기 81.42%, 덤프트럭 78.23%를 나타냈다. ‘B’ 현장의 이동식 크레인은 78.14%의 평균 정확도를 보여줬다. 결론: 현장 안전관리의 효율성이 증가할 수 있고, 재해발생 위험요인을 최소화 할 수 있을것이라 본다. 또한, 본 연구를 기반으로 건설현장에 스마트 건설기술 도입에 관한 기초적인 자료로 활용이 가능하다.

키워드

컴퓨터비전

YOLACT

건설장비

객체 추출

안전관리

References

An, H.J. (2022). A study on the Duration Estimation of Construction Activity Using Deep Learning Algorithm. Master Thesis, Gyeonsang National University.
Arabi, S., Haghighat, A., Sharma, A. (2020). "A deep‐learning‐based computer vision solution for construction vehicle detection." Computer‐Aided Civil and Infrastructure Engineering, Vol. 35, No. 7, pp. 753-767. 10.1111/mice.12530
Choi, S.I., Lee, G.P. (2019). The Direction of Legislation to Revitalize Smart Construction Technology. CERIK, Vol. 2019, No. 2, Seoul, Republic of Korea.
Ha, M.H. (2021). Development of Intelligent Construction Inspection Model Using Deep Learning-based Object Detection Technology. Master Thesis, Koreatech.
Kim, J.D. (2022). Identifying Indoor Location of Close-up Photos Using Deep Learning and Building Information Modeling Object. Master Thesis, Yonsei University.
Kim, S.H. (2022). Method of Deep-Learning based Automatic Generation of Object Material Data for Scan-to-BIM Automation. Master Thesis, Sejong University.
Lee, G.P., Choi, S.Y., Sohn, T.H., Choi, S.I. (2019). Survey on Smart Technology Applications of Korean Construction Companies and Strategies for Activation. CERIK, Vol. 2019 No.-, Seoul, Republic of Korea.
Lee, Y.S. (2022). Long and Short Term Prediction of Rebar Price Using Deep learning and Related Techniques. Master Thesis, Konkuk University.
Ministry of Employment and Labor (2022). Large-scale Construction Site Machinery and Equipment Deaths Caution. Sejong.
Ministry of Employment and Labor (2023). Additional Statistics on the Status of Industrial Accidents in 2022 (Current Status of Deaths Subject to Disaster Investigation). Sejong.
National Law Information Center (2021). Occupational Safety and Health Act "Law No. 18039". pp. 1-46.
Park, J.Y. (2021). A Study on the Prediction of Steel Quantities of Qteel Qtructures based on a Deep Learning Algorithm. Master Thesis, Korea University.
Yan, X., Zhang, H., Li, H. (2020). Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents. Computer‐Aided Civil and Infrastructure Engineering, Vol. 35, No. 9, pp. 1023-1038. 10.1111/mice.12536

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 19
No :4
Pages :916-923
DOI :https://doi.org/10.15683/kosdi.2023.12.31.916

[1] An, H.J. (2022). A study on the Duration Estimation of Construction Activity Using Deep Learning Algorithm. Master Thesis, Gyeonsang National University.

[2] Arabi, S., Haghighat, A., Sharma, A. (2020). "A deep‐learning‐based computer vision solution for construction vehicle detection." Computer‐Aided Civil and Infrastructure Engineering, Vol. 35, No. 7, pp. 753-767. 10.1111/mice.12530

[3] Choi, S.I., Lee, G.P. (2019). The Direction of Legislation to Revitalize Smart Construction Technology. CERIK, Vol. 2019, No. 2, Seoul, Republic of Korea.

[4] Ha, M.H. (2021). Development of Intelligent Construction Inspection Model Using Deep Learning-based Object Detection Technology. Master Thesis, Koreatech.

[5] Kim, J.D. (2022). Identifying Indoor Location of Close-up Photos Using Deep Learning and Building Information Modeling Object. Master Thesis, Yonsei University.

[6] Kim, S.H. (2022). Method of Deep-Learning based Automatic Generation of Object Material Data for Scan-to-BIM Automation. Master Thesis, Sejong University.

[7] Lee, G.P., Choi, S.Y., Sohn, T.H., Choi, S.I. (2019). Survey on Smart Technology Applications of Korean Construction Companies and Strategies for Activation. CERIK, Vol. 2019 No.-, Seoul, Republic of Korea.

[8] Lee, Y.S. (2022). Long and Short Term Prediction of Rebar Price Using Deep learning and Related Techniques. Master Thesis, Konkuk University.

[9] Ministry of Employment and Labor (2022). Large-scale Construction Site Machinery and Equipment Deaths Caution. Sejong.

[10] Ministry of Employment and Labor (2023). Additional Statistics on the Status of Industrial Accidents in 2022 (Current Status of Deaths Subject to Disaster Investigation). Sejong.

[11] National Law Information Center (2021). Occupational Safety and Health Act "Law No. 18039". pp. 1-46.

[12] Park, J.Y. (2021). A Study on the Prediction of Steel Quantities of Qteel Qtructures based on a Deep Learning Algorithm. Master Thesis, Korea University.

[13] Yan, X., Zhang, H., Li, H. (2020). Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents. Computer‐Aided Civil and Infrastructure Engineering, Vol. 35, No. 9, pp. 1023-1038. 10.1111/mice.12536

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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