The construction industry is witnessing a transformative shift with the adoption of computer vision (CV) technologies for productivity monitoring and performance evaluation. This review consolidates the advancements in CV applications, focusing on areas, such as real-time progress tracking, resource management, and safety monitoring. The analysis highlights how automated systems, powered by deep learning models and object detection algorithms, are replacing traditional manual methods, offering enhanced accuracy, efficiency, and decision-making capabilities. Despite these advancements, challenges, such as scalability, variability in construction environments, and limited system integration remain barriers to widespread adoption. Furthermore, critical gaps, including the limited use of advanced CV models for multi-resource tracking and the lack of intuitive interfaces for real-time visualization, are identified. This study underscores the necessity for further innovation and research to address these limitations, enabling the construction industry to harness the full potential of CV technologies. The findings offer valuable insights into how CV can drive a data-centric approach to safer and more efficient construction practices.

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1. Teizer Jochen. Status quo and open challenge in vision based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics. Elsevier; 2015.
2. Yang J, Park MW, Vela PA, et al. Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics. Elsevier; 2015.
3. Moragane HPNMLB, Perera BAKS, Palihakkara AD. Application of computer vision for construction progress monitoring. Proceedings of the 10th World Construction Symposium; 2022.
4. Rehman MSR, Shafiq MT, Ullah F. Automated computer vision based construction progress monitoring: A systematic review. Buildings. 2022.
5. Bozorgzadeh A, Umar T. Automated progress measurement using computer vision technology in UK construction. Smart Infrastructure and Construction. Emerald Publishing Limited; 2023.
6. Al-Sinan MA, Bubshait AA, Aljaroudi Z. Generation of construction scheduling through machine learning and BIM: A blueprint. Buildings. 2024.
7. Zhang X, Bakis N, Lukins TC, et al. Automating progress measurement of construction projects. Automation in Construction. Elsevier; 2009.
8. Gong J, Caldas CH. Computer vision-based video interpretation model for automated productivity analysis of construction operations. Journal of Computing in Civil Engineering; 2010.
9. Dimitrov A, Golparvar-Fard M. Vision based material recognition for automated monitoring of construction progress and generating building information modelling from unordered site image collection. Advanced Engineering Informatics. Elsevier; 2013.
10. Hamledari H, McCabe B, Davari S. Automated computer vision-based detection of components of under-construction indoor partitions. Automation in Construction. Elsevier; 2016.
11. Luo H, Xiong C, Fang W, et al. Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction. Elsevier; 2018.
12. Arabi S, Haghighat A, Sharma A. A deep learning based computer vision solution for construction vehicle detection. Computer Aided Civil and Infrastructure Engineering; 2020.
13. Pour Rahimian F, Seyedzadeh S, Oliver S, et al. On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Automation in Construction; 2019.
14. Nath ND, Behzadan AH. Deep convolutional networks for construction object detection under different visual conditions. Frontiers in Built Environment; 2020. doi: 10.3389/fbuil.2020.00097.
15. Wang Z, Zhang Q, Yang B, et al. Vision-based framework for automatic progress monitoring of precast walls by using surveillance videos during the construction phase. Journal of Computing in Civil Engineering, ASCE; 2020. doi: 10.1061/(ASCE)CP.1943-5487.0000933.
16. Bhokare S, Goyal L, Ren R, et al. Smart construction scheduling monitoring using YOLO V3-based activity detection and classification. Journal of Information Technology in Construction; 2022.
17. Chen C, Gu H, Lian S, et al. Investigation of edge computing in computer vision based construction resource detection. Buildings; 2022.
18. Yang JS, Wilde A, Menzel K, et al. Computer vision for construction progress monitoring: A real-time object detection approach. Intelligence Empowerment of Construction Industry; 2023.
19. Lung LW, Wang YR. Applying deep learning and single shot detection in construction site image recognition. Buildings; 2023.
20. Fang Q, Li H, Luo X, et al. A deep learning - based method for detecting non-certified work on construction sites. Advanced Engineering Informatics. Elsevier; 2018.

The construction industry is witnessing a transformative shift with the adoption of computer vision (CV) technologies for productivity monitoring and performance evaluation. This review consolidates the advancements in CV applications, focusing on areas, such as real-time progress tracking, resource management, and safety monitoring. The analysis highlights how automated systems, powered by deep learning models and object detection algorithms, are replacing traditional manual methods, offering enhanced accuracy, efficiency, and decision-making capabilities. Despite these advancements, challenges, such as scalability, variability in construction environments, and limited system integration remain barriers to widespread adoption. Furthermore, critical gaps, including the limited use of advanced CV models for multi-resource tracking and the lack of intuitive interfaces for real-time visualization, are identified. This study underscores the necessity for further innovation and research to address these limitations, enabling the construction industry to harness the full potential of CV technologies. The findings offer valuable insights into how CV can drive a data-centric approach to safer and more efficient construction practices.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Teizer Jochen. Status quo and open challenge in vision based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics. Elsevier; 2015.
2. Yang J, Park MW, Vela PA, et al. Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics. Elsevier; 2015.
3. Moragane HPNMLB, Perera BAKS, Palihakkara AD. Application of computer vision for construction progress monitoring. Proceedings of the 10th World Construction Symposium; 2022.
4. Rehman MSR, Shafiq MT, Ullah F. Automated computer vision based construction progress monitoring: A systematic review. Buildings. 2022.
5. Bozorgzadeh A, Umar T. Automated progress measurement using computer vision technology in UK construction. Smart Infrastructure and Construction. Emerald Publishing Limited; 2023.
6. Al-Sinan MA, Bubshait AA, Aljaroudi Z. Generation of construction scheduling through machine learning and BIM: A blueprint. Buildings. 2024.
7. Zhang X, Bakis N, Lukins TC, et al. Automating progress measurement of construction projects. Automation in Construction. Elsevier; 2009.
8. Gong J, Caldas CH. Computer vision-based video interpretation model for automated productivity analysis of construction operations. Journal of Computing in Civil Engineering; 2010.
9. Dimitrov A, Golparvar-Fard M. Vision based material recognition for automated monitoring of construction progress and generating building information modelling from unordered site image collection. Advanced Engineering Informatics. Elsevier; 2013.
10. Hamledari H, McCabe B, Davari S. Automated computer vision-based detection of components of under-construction indoor partitions. Automation in Construction. Elsevier; 2016.
11. Luo H, Xiong C, Fang W, et al. Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction. Elsevier; 2018.
12. Arabi S, Haghighat A, Sharma A. A deep learning based computer vision solution for construction vehicle detection. Computer Aided Civil and Infrastructure Engineering; 2020.
13. Pour Rahimian F, Seyedzadeh S, Oliver S, et al. On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Automation in Construction; 2019.
14. Nath ND, Behzadan AH. Deep convolutional networks for construction object detection under different visual conditions. Frontiers in Built Environment; 2020. doi: 10.3389/fbuil.2020.00097.
15. Wang Z, Zhang Q, Yang B, et al. Vision-based framework for automatic progress monitoring of precast walls by using surveillance videos during the construction phase. Journal of Computing in Civil Engineering, ASCE; 2020. doi: 10.1061/(ASCE)CP.1943-5487.0000933.
16. Bhokare S, Goyal L, Ren R, et al. Smart construction scheduling monitoring using YOLO V3-based activity detection and classification. Journal of Information Technology in Construction; 2022.
17. Chen C, Gu H, Lian S, et al. Investigation of edge computing in computer vision based construction resource detection. Buildings; 2022.
18. Yang JS, Wilde A, Menzel K, et al. Computer vision for construction progress monitoring: A real-time object detection approach. Intelligence Empowerment of Construction Industry; 2023.
19. Lung LW, Wang YR. Applying deep learning and single shot detection in construction site image recognition. Buildings; 2023.
20. Fang Q, Li H, Luo X, et al. A deep learning - based method for detecting non-certified work on construction sites. Advanced Engineering Informatics. Elsevier; 2018.