Adaptive building engineering component extraction model based on DSOD

• Autorzy: Lv Na , Yang Xuan

Identyfikatory

DOI

10.24425/ace.2025.154125

• Języki publikacji: EN

Abstrakty

EN

With the purpose to bring up the extraction efficiency and accuracy of building construction image component information, the dense block structure and loss function were proposed to optimize the deep supervised object detection algorithm, and an adaptive building construction component extraction model based on this algorithm was constructed. The improved depth-supervised target detection algorithm constructed by the study is validated and found to have an accuracy of 87.4% and a precision of 0.84, which is better than other comparative algorithms. The effectiveness of the adaptive extraction model of building components constructed by the research is verified, and it is found that the extraction error of the model is 9.8%, the value of the loss function is 0.2, and the satisfaction score of the experts is 8.8, and its extraction accuracy and efficiency are better than that of the other models, and it can satisfy the demand for the extraction of components of the construction project. In summary, it can be seen that the adaptive extraction model of building components constructed by the research has excellent information extraction performance, not only can it improve the efficiency of extracting engineering components, but it can also significantly enhance the decision support ability in construction management, optimize resource allocation, reduce risks, and improve the management efficiency of engineering projects. It has a positive contribution to the theory and practice of construction management discipline.

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2025
• Tom: Vol. 71, nr 2
• Strony: 345--361
• Opis fizyczny: Bibliogr. 21 poz., il., tab.

Twórcy

autor

Lv Na

• Xinxiang Vocational and Technical College, School of Architecture, Xinxiang, China

• https://orcid.org/0009-0002-3097-2313

autor

Yang Xuan

• Xinxiang Vocational and Technical College, School of Architecture, Xinxiang, China

• https://orcid.org/0009-0004-3235-5663

Bibliografia

• [1] Z. Zhou, Y. Guo, M. Dai, J. Huang, and X. Li, “Weakly supervised salient object detection via double object proposals guidance”, IET Image Processing, vol. 15, no. 9, pp. 1957-1970, 2021, doi: 10.1049/ipr2.12164.
• [2] S. Guo, K. Wang, H. Kang, Y. Zhang, Y. Gao, and T. Li, “BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation”, International Journal of Medical Informatics, vol. 126, no. 6, pp. 105-113, 2019, doi: 10.1016/j.ijmedinf.2019.03.015.
• [3] W. Bo, Y. Lei, S. Tian, et al., “Deeply supervised 3D FCN with group dilated convolution for automatic MRI prostate segmentation”, Medical Physics, vol. 46, no. 4, pp. 1707-1718, 2019, doi: 10.1002/mp.13416.
• [4] G. Wu, Y. Liu, C. Liu, Z. Zheng, Y. Cui, “Seismic data interpolation using deeply supervised U-Net++ with natural seismic training sets”, Geophysical Prospecting, vol. 71, no. 2, pp. 227-244, 2023, doi: 10.1111/1365-2478.13307.
• [5] J. Loic, N.S. Vu, J. Beaudet, and A. Histace, “Efficient anomaly detection using self-supervised multi-cue tasks”, IEEE Transactions on Image Processing, vol. 32, no. 1, pp. 807-812, 2023, doi: 10.1109/TIP.2022.3231532.
• [6] Y. Ji, H. Zhang, F. Gao, et al., “LGCNet: A local-to-global context-aware feature augmentation network for salient object detection”, Information Sciences, vol. 584, no. 1, pp. 399-416, 2022, doi: 10.1016/j.ins.2021.10.055.
• [7] H. Ma, X. Li, X. Yuan, and C. Zhao, “Two-phase self-supervised pretraining for object re-identification”, Knowledge-Based Systems, vol. 261, no. 15, pp. 1-12, 2023, doi: 10.1016/j.knosys.2022.110220.
• [8] J. Konior, “Technical assessment of old buildings by probabilistic approach”, Archives of Civil Engineering, vol. 66, no. 3, pp. 443-466, 2020, doi: 10.24425/ace.2020.134407.
• [9] D. Mishra and P. Aswathy, “Harnessing feedback region proposals for multi-object tracking”, IET Computer Vision, vol. 14, no. 7, pp. 434-442, 2020, doi: 10.1049/iet-cvi.2019.0943.
• [10] P. Kumar, V. Kumar, and R. Pratap, “FPGA implementation of an Islanding detection technique for microgrid using periodic maxima of superimposed voltage components”, IEET Generation, Transmission & Distribution, vol. 14, no. 9, pp. 1673-1683, 2020, doi: 10.1049/iet-gtd.2018.5914.
• [11] F. Stern, J. Kleinhorst, J. Tenkamp, and F. Walther, “Investigation of the anisotropic cyclic damage behavior of selective laser melted AISI 316L stainless steel”, Fatigue & Fracture of Engineering Materials & Structures, vol. 42, no. 11, pp. 2422-2430, 2019, doi: 10.1111/ffe.13029.
• [12] H. Esmaeeli, R. Marandi, M. Karimi, and C. Alecsandru, “Driving impairment detection due to sun exposure and contrasting shadow of surface objects: An urban case study”, Canadian Journal of Civil Engineering, vol. 49, no. 3, pp. 401-411, 2022, doi: 10.1139/cjce-2020-0836.
• [13] S. Skibicki, T. Wróblewski, W. Paczkowski, et al., “Reinforcement solution of damaged load-bearing frame structure in a coal power plant for additional loads”, Archives of Civil Engineering, vol. 70, no. 1, pp. 121-139, 2024, doi: 10.24425/ace.2024.148903.
• [14] Y. Zhang and X. Li, “Two-step support vector data description for dynamic, non-linear, and non-Gaussian processes monitoring”, The Canadian Journal of Chemical Engineering, vol. 98, no. 10, pp. 2109-2124, 2020, doi: 10.1002/cjce.23762.
• [15] B. Aykut and C. Hanili, “Deep convolutional neural networks for double compressed AMR audio detection”, IET Signal Processing, vol. 15, no. 4, pp. 265-280, 2021, doi: 10.1049/sil2.12028.
• [16] P. Popielski, B. Bednarz, T. Majewski, and M. Niedostatkiewicz, “Settlement of a historic building due to seepage-induced soil deformation”, Archives of Civil Engineering, vol. 69, no. 2, pp. 65-82, 2023, doi: 10.24425/ace.2023.145253.
• [17] D. Dworakowski, A. Fung, and G. Nejat, “Robots Understanding contextual information in human-centered environments using weakly supervised mask data distillation”, International Journal of Computer Vision, vol. 131, no. 2, pp. 407-430, 2023, doi: 10.1007/s11263-022-01706-5.
• [18] Y. Wang, R. Xiong, P. Tang, and Y. Liu, “Fast and reliable map matching from large-scale noisy positioning records”, Journal of Computing in Civil Engineering, vol. 37, no. 1, pp. 1-14, 2023, doi: 10.1061/(ASCE)CP.1943-5487.0001054.
• [19] J. Li, Z. Han, J. Liu, Y. Zou, and X. Xu, “Compositional gradient engineering and applications in halide perovskites”, Chemical Communications, vol. 59, no. 35, pp. 5159-5173, 2023, doi: 10.1039/d3cc00967j.
• [20] E. Nsugbe, “Toward a self-supervised architecture for semen quality prediction using environmental and lifestyle factor”, Artificial Intelligence and Applications, vol. 1, no. 1, pp. 35-42, 2023, doi: 10.47852/bonviewAIA2202303.
• [21] A. Imron and A.E. Husin, “Value engineering and lifecycle cost analysis to improve cost performance in green hospital project”, Archives of Civil Engineering, vol. 67, no. 4, pp. 497-510, 2021, doi: 10.24425/ace.2021.138514.