Academic Journal of Engineering and Technology Science, 2025, 8(3); doi: 10.25236/AJETS.2025.080303.
Hong Zhang, Shengwei Liu
Institute of Intelligent Construction and Engineering Management, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
In prefabricated construction projects, the supply of prefabricated components is closely related to the project construction progress. Shortage of inventory will delay construction progress, and excessive inventory on the construction site will increase stacking costs. Therefore, on-site inventory management of prefabricated components is crucial for prefabricated construction. This paper employs computer vision to precisely identify the types and quantities of prefabricated components on construction sites. Furthermore, inventory management theory is combined to establish a dynamic inventory management optimization model for prefabricated components. Ultimately, the particle swarm optimization is utilized to determine and obtain the optimal inventory parameters. A dynamic inventory management model for prefabricated components based on real-time monitoring is proposed, and demonstrated and validated through case study. The results can facilitate the progress of prefabricated construction projects as planned, expanding the integration and application prospects of computer vision and inventory management theory in the field of construction management.
Prefabricated building; Prefabricated component; Inventory management; Computer vision; Particle swarm optimization
Hong Zhang, Shengwei Liu. Dynamic inventory management of prefabricated components using computer vision. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 3: 13-22. https://doi.org/10.25236/AJETS.2025.080303.
[1] Zhai P, Wang J, Zhang L, Extracting worker unsafe behaviors from construction images using image captioning with deep learning–based attention mechanism. Journal Of Construction Engineering And Management. 2023:04022164.
[2] Yu Y, Li H, Cao J, Luo X, Three-dimensional working pose estimation in industrial scenarios with monocular camera. IEEE international conference on computer vision. 2021, Things 8 (3):1740–1748.
[3]Chen C, Xiao B, Zhang Y, Zhu Z, Automatic vision-based calculation of excavator earthmoving productivity using zero-shot learning activity recognition. Automation in Construction. 2023, 146:104702.
[4] Liu H, Wang D, Xu K, Zhou P, Zhou D, Lightweight convolutional neural network for counting densely piled steel bars. Automation in Construction.2023, 146:104692.
[5] Yan X, Zhang H, Gao H, Mutually coupled detection and tracking of trucks for monitoring construction material arrival delays. Automation in Construction. 2022,142:104491.
[6] Yan X, Zhang H, Computer vision-based disruption management for prefabricated building construction schedule. Journal Of Computing In Civil Engineering. 2021,35(6):04021027.
[7] Yan X, Zhang H, Zhang W, Intelligent monitoring and evaluation for the prefabricated construction schedule. Computer-aided Civil And Infrastructure Engineering. 2022, 38 (3):1–17.
[8] Lu L, Dai F, Automated visual surveying of vehicle heights to help measure the risk of over height collisions using deep learning and view geometry. Computer-aided Civil And Infrastructure Engineering. 2022, 38 (2):194–210.
[9] Zhang E, Shao L, Wang Y, Unifying transformer and convolution for dam crack detection. Automation in Construction. 2023, 147 :104712.
[10] Qiu Q, Lau D, Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images. Automation in Construction. 2023, 147:104745.
[11] Yang X, Guo R, Li H, Comparison of multimodal RGB-thermal fusion techniques for exterior wall multi-defect detection. Journal of Infrastructure Intelligence and Resilience. 2023:100029.
[12] Zhang W, Zhang H, Yu L. Collaborative Planning for Stacking and Installation of Prefabricated Building Components Regarding Crane-Collision Avoidance. Journal of Construction Engineering and Management. 2023,149(6).
[13] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016:779-788.
[14] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition.2017:7263-7271.
[15] Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767,2018.
[16] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934,2020.
[17] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. European conference on computer vision.Springer,Cham,2016:21-37.
[18] Jeong J, Park H, Kwak N. Enhancement of SSD by concatenating feature maps for object detection. arXiv preprint arXiv:1705.09587,2017.
[19] Shen Z, Liu Z, Li J, et al. Dsod: Learning deeply supervised object detectors from scratch. Proceedings of the IEEE international conference on computer vision.2017:1919-1927.
[20] Li Z, Zhou F. FSSD: feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960,2017.
[21] Luo, X., Li, H., Yang, X., et al. Capturing and Understanding Workers’ Activities in Far Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning. Computer-Aided Civil and Infrastructure Engineering, 2019, 34 (4): 333-351.
[22] Yan X, Zhang H, Zhang W, Intelligent monitoring and evaluation for the prefabricated construction schedule. Computer-aided Civil And Infrastructure Engineering. 2022, 38 (3):1–17.
[23] Yan, X. Z., Li, H., Li, A. R., et al. Wearable IMU-Based Real-Time Motion Warning System for Construction Workers' Musculoskeletal Disorders Prevention . Automation in Construction. 2017, 74: 2-11.
[24] Zhang, H., Yan, X. Z. & Li, H. Ergonomic Posture Recognition Using 3D View-Invariant Features from Single Ordinary Camera. Automation in Construction. 2018, 94: 1-10.
[25] Fang, Q., Li, H., Luo, X., et al. Detecting Non-Hardhat-Use by a Deep Learning Method from Far-Field Surveillance Videos. Automation in Construction. 2018, 85: 1-9.
[26] Cha, Y.-J., Choi, W. & Büyüköztürk, O. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil and Infrastructure Engineering. 2017, 32 (5): 361-378.
[27] Li, S., Zhao, X. & Zhou, G. Automatic Pixel-Level Multiple Damage Detection of Concrete Structure Using Fully Convolutional Network. Computer-Aided Civil and Infrastructure Engineering. 2019, 34 (7): 616-634.
[28] Yang, X., Li, H., Yu, Y., et al. Automatic Pixel-Pevel Crack Detection and Measurement Using Fully Convolutional Network . Computer-Aided Civil and Infrastructure Engineering. 2018, 33 (12): 1090-1109.
[29] Xue, Y. & Li, Y. A Fast Detection Method Via Region-Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects . Computer-Aided Civil and Infrastructure Engineering. 2018, 33 (8): 638-654
[30] Bang, S., Park, S., Kim, H., et al. Encoder-Decoder Network for Pixel-Level Road Crack Detection in Black‐Box Images . Computer-Aided Civil and Infrastructure Engineering. 2019, 34 (8): 713-727.
[31] Maeda, H., Sekimoto, Y., Seto, T., et al. Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images . Computer-Aided Civil and Infrastructure Engineering. 2018, 33 (12): 1127-1141.
[32] Fang, Q., Li, H., Luo, X., et al. Detecting Non-Hardhat-Use by a Deep Learning Method from Far-Field Surveillance Videos. Automation in Construction. 2018, 85: 1-9.
[33] Kim, H., Ham, Y., Kim, W., et al. Vision-Based Nonintrusive Context Documentation for Earthmoving Productivity Simulation. Automation in Construction. 2019, 102: 135-147.
[34] Kim, H., Kim, H., Hong, Y. W., et al. Detecting Construction Equipment Using a Region Based Fully Convolutional Network and Transfer Learning. Journal of Computing in Civil Engineering. 2018, 32 (2): 04017082.
[35] Kolar Z, Chen H, Luo X. Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction. 2018, 89:58-70.
[36] H. Liu, D. Wang, K. Xu, P. Zhou, D. Zhou, Lightweight convolutional neural network for counting densely piled steel bars. Automation in Construction. 2023, 146:104692.
[37] X. Yan, H. Zhang, H. Gao, Mutually coupled detection and tracking of trucks for monitoring construction material arrival delays. Automation in Construction. 2022, 142:104491.
[38] Li H, Love P. Site-Level Facilities Layout Using Genetic Algorithms. Journal of Computing in Civil Engineering. 1998, 12(4): 227-31.
[39] Alanjari P, Razavialavi S, Abourizk S M. Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown. Journal of Construction Engineering & Management. 2015, 141(10): 06015001.
[40] K. Wada, Labelme. https://github.com/wkentaro/labelme, 2016.