## 2 Dataset Establishment  
Due to the absence of publicly available reinforcing mesh datasets, it is imperative to construct a dataset consisting of a large number of annotated images of reinforcing mesh. During data collection, camera equipment was employed to capture original images of reinforcing mesh in real-world engineering projects. The diversity and representativeness of the dataset are crucial to the model's performance. Consequently, reinforcing mesh images were collected from various construction environments, including different scenes such as bridges, tunnels, and roads, as well as samples of different forms and densities of reinforcing meshes and cages. The image resolution in the dataset ranges from 1920 \(\times\) 1080 to 3840 \(\times\) 2160, ensuring that the model can handle high-resolution data.  
To enhance the diversity of the dataset, factors that affect detection and dimension calculation accuracy were considered during the image collection process. As shown in Figure 1, images (a) and (b) reflect differences in the complexity of the background, while images (c) and (d) illustrate the contrast between the background and the color of the reinforcing mesh. Additionally, images (e) and (f) depict situations where shadows and reflective spots caused by the reinforcing mesh are present.  
After acquiring the original images of the reinforcing mesh, manual annotation was performed using the open-source labeling software Labelme3.16.7 to obtain the mask labels for the reinforcement. A polygon was used to outline the edges of each instance of reinforcement, and the enclosed area of the polygon was considered the reinforcement mask. Each individual rebar, node position, and its geometric attributes within the mesh were precisely annotated. Moreover, the dataset includes various complexities commonly encountered in construction environments, such as reflections on the reinforcement surface, coatings, and stains, to enhance the model's robustness and adaptability.  
A total of more than 9000 images were initially collected. To improve the generalization ability of the model and reduce the similarity between the data, a combination of data augmentation techniques was applied to expand the dataset. These techniques included geometric transformations such as image translation, flipping, and affine transformations, as well as pixel-level transformations like brightness and contrast adjustment, and the addition of Gaussian noise. As shown in Figure 2(1), the original image from the dataset was transformed through horizontal flipping and contrast adjustment, resulting in Figure 2(2), while cropping, magnification, and Gaussian noise were applied to produce Figure 2(3). After augmentation, images with excessive similarity were further filtered out, and a final count of 21,000 images was retained, meeting the data volume requirements for neural network training. To maximize the use of the data for model performance testing, the dataset was divided into training (70%), validation (15%), and testing (15%) sets [35].  
Figure 1: Rebar mesh images with complex backgrounds: (**a**,**b**) overlapping rebars; (**c**,**d**) color differences among different types of Rebars; (**e**,**f**) obvious shadows behind the Rebars.