## 2 Related Work  
As the construction industry demands higher efficiency and safety, manual rebar tying faces challenges such as low productivity and significant safety risks. To address these issues, rebar tying robots have been developed to automate the process, improving both precision and efficiency while reducing physical labor. These robots not only lower labor costs but also enhance the quality of the tying process. High-quality rebar tying is critical for structural durability and construction safety [18]. With advances in robotics, extensive research has been conducted on vision recognition, path planning, and motion control, providing a solid foundation for the practical application of rebar tying robots. Several autonomous rebar tying robots have been successfully deployed on construction sites, each with specific advantages and limitations. TyBOT, developed by Advanced Construction Robotics, targets large-scale infrastructure projects such as bridges and roads. It can complete more than 1200 ties per hour in all weather conditions, greatly improving efficiency and reducing labor demands. However, its use is mainly limited to standardized scenarios and it struggles with irregular rebar arrangements or smaller projects. In addition, its high cost and sensitivity to factors like lighting variations and corroded rebar hinder wider adoption [19]. SkyTy, developed by SkyMul, leverages drone technology for automated rebar tying. It consists of a ground unit, a mapping drone, and multiple working drones, aiming to cut labor costs, reduce errors, and speed up construction. Yet, as a new technology, SkyTy still faces challenges in recognition accuracy and operational stability in complex environments, while high equipment and training costs further restrict its use [20]. Similarly, T-iROBO Rebar, developed by Taisei Corporation, can accurately detect rebar intersections and perform tying tasks, improving both efficiency and safety. However, it requires a controlled environment and has limited adaptability to complex or irregular layouts, and its high cost also limits widespread implementation [21]. In vision recognition, researchers have improved the accuracy of rebar detection and localization by integrating computer vision and deep learning methods. [PERSON] et al. proposed a fast synthetic dataset generation approach using Building Information Modeling (BIM) and rendering software for rebar instance segmentation. By automating data annotation, this method greatly reduced the time and cost of creating training datasets. They trained a Mask R-CNN model with mixed real and synthetic data in various proportions, demonstrating that synthetic data can enhance prediction performance and provide a certain level of generalization. However, this approach focuses mainly on 2D segmentation and does not address detailed 3D attributes such as position, spacing, and diameter, limiting its use in rebar tying robots [22]. [PERSON] et al. developed an innovative planar rebar tying robot and introduced a two-stage recognition method that combines depth cameras and industrial cameras. By using the YOLOv5 deep learning model and the DBSCAN clustering algorithm, they achieved accurate detection and condition assessment of rebar intersections. Autonomous navigation and path planning were implemented with the ROS system. Experiments on both lab platforms and actual construction sites verified the system's effectiveness. However, this method still faces challenges related to adaptation in complex environments, real-time performance, long-term stability, and cost efficiency [23]. In rebar intersection detection, deep learning methods provide high accuracy but have drawbacks such as large data requirements, high computational costs, and limited robustness to environmental variations. As an alternative, Hough Transform-based detection offers simplicity, computational efficiency, and lower data demands, making it suitable for fast rebar intersection detection.