###### Abstract  
Among various building information model (BIM) reconstruction methods for existing building, image-based method can identify building components from scanned as-built drawings and has won great attention due to its lower cost, less professional operators and better reconstruction performance. However, this kind of method will cost a great deal of time to design and extract features. Moreover, the manually extracted features have poor robustness and contain less non-geometric information. In order to solve this problem, this paper proposes a deep learning-based method to detect building components from scanned 2D drawings. Taking structural drawings as an example, in this article, 1500 images of structural drawings were firstly collected and preprocessed to guarantee the quality of data. After that, the neural network model--You Only Look Once (YOLO) was trained, verified and tested. In addition, a series of metrics were utilized to evaluate the performance of recognition. The results of test experiments show that the components in structural drawings (e.g., grid reference, column and beam) can be successfully detected, while the average detection accuracy of the whole image is over 80% and the average detection time for each image is 0.71 s. The experimental results demonstrate that the proposed method is robust and timesaving, which provides a good basis for the reconstruction of BIM from 2D drawings. 3D Reconstruction; BIM; 2D structural drawing; object detection; deep learning; YOLO 100 100  
physical information, while the precision for single building is lower compared to other methods. Laser scanning [7] can efficiently create 3D models of existing buildings through point clouds, however, it is invalid when dealing with the components hidden above ceilings or behind walls [8]. In addition, the cost of using laser scanning devices is relatively high [1]. Other methods like reconstructing 3D models based on 2D vector drawings [9] can extract not only geometrical information but also topological and semantic information of buildings. The downside of this method is that there are a number of drafting errors [10; 11] in CAD drawings, which will definitely have a bad effect on the precision of reconstruction models. Moreover, a large proportion of existing buildings have been built for more than 20 years, so instead of CAD vector drawings, only paper-based drawings or hand-drawn blueprints may remain [12]. Different from the methods mentioned above, the method that scans CAD drawings or hand-drawn blueprints into digital formats and extracts information from those raster images using image processing technique [5] has received more attention from researchers due to its high efficiency, low cost and widespread applicability. As the first step of an image-based method, component recognition plays a significant role in reconstructing BIM for existing buildings. Traditionally, geometric primitives (lines, polylines, arcs, etc.) obtained by feature extraction are combined into building components based on predefined definitions about geometric constraints and topological relations [13]. However, components may be represented in different ways due to the diversity of drawing standards and drawing conventions in different countries [9]. As a result, the traditional component recognition approach, which predefines the rules, cannot satisfy all kinds of expressions. Therefore, a smarter method that can be widely applied to various conditions is needed to identify building components from raster images of CAD drawings [14]. In this paper, a novel method that automatically detects structural components from scanned CAD drawings was proposed based on a convolutional neural network called You Only Look Once (YOLO).