Adaptive building engineering component extraction model based on DSOD

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.