## 1 Introduction  
With the acceleration of global urbanization, the number and scale of civil engineering projects have increased continuously, and the complexity of engineering structures has gradually escalated [1]. In these projects, reinforcing mesh serves as a critical component in concrete structures, playing a key role in enhancing structural strength and prolonging service life [2, 3]. Consequently, the identification and detection of reinforcing mesh are of great significance for ensuring construction quality and safety [4, 5, 6, 7]. Methods for identifying reinforcing mesh can be categorized into two main types: contact-based and non-contact-based detection [8]. Contact-based detection typically relies on a substantial number of on-site operators who visually inspect or use simple tools to determine the distribution of the reinforcing mesh. This approach is not only time-consuming and labor-intensive but also lacks stability and accuracy due to the varying professional skills and experience levels of the operators. Additionally, the complexity of the construction environment and variations in the density of the reinforcing mesh, surface coatings, or stains on the reinforcement further increase the difficulty of manual detection,thereby reducing accuracy. In contrast, non-contact-based detection methods, primarily based on deep learning algorithms in computer vision, can extract feature information from images [9,10]. Compared with contact-based methods, automated identification techniques using computer vision generally offer higher efficiency and precision [11, 12, 13, 14, 15]. As a commonly employed non-destructive testing method, the machine vision-based identification of reinforcing mesh is widely applied in practical engineering due to its simplicity of operation and cost-effectiveness [16, 17, 18]. However, in environments with dense reinforcing mesh, this approach still faces accuracy challenges. Therefore, improving the ability to identify reinforcing mesh in complex backgrounds has become one of the primary research focuses [19]. Upon the completion of the identification and segmentation of the reinforcing mesh, geometric information statistics constitute a key step in engineering applications [20, 21, 22]. Geometric information statistics involve the extraction and analysis of data such as the spacing between reinforcements, which holds critical significance for engineering design and construction quality control [23, 24, 25]. For instance, the accurate calculation of reinforcement spacing directly impacts the strength and stability of concrete structures [26, 27, 28, 29]. In traditional engineering practices, these geometric data often depend on manual measurements and calculations, a process that is cumbersome and prone to errors [30]. The application of modern image processing technologies enables the automation of geometric information statistics. Through the geometric analysis of the identified reinforcing mesh combined with image processing algorithms, automatic calculations of reinforcement spacing can be performed. This process not only enhances statistical efficiency but also significantly improves data accuracy by reducing the influence of human error [31, 32, 33]. Nevertheless, the accuracy of geometric information statistics still faces challenges, particularly in scenarios where imprecise segmentation results from complex backgrounds lead to a decline in statistical precision. Hence, developing efficient and accurate methods for geometric information statistics, especially for the extraction of geometric data from reinforcing mesh in complex environments, remains an essential task in the current research [34]. In summary, this study aims to develop an algorithm for the identification of reinforcing mesh and geometric information statistics based on deep learning to achieve the efficient and accurate recognition and segmentation of reinforcing mesh, followed by geometric data analysis.