## 1 Introduction  
With the rapid development of the construction industry and increasing labor costs, integrating robotics into construction processes has attracted growing interest [1]. Robotics can greatly improve construction efficiency, enhance accuracy, and reduce workplace accidents. Traditionally, the construction industry has relied heavily on labor-intensive, repetitive work in hazardous environments, making robotics a promising solution to these issues [2, 3]. Advances inconstruction robotics not only promote automation but also expand its applications--from material transport to the precise assembly of structural elements [4]. The use of robotics helps to streamline workflows, boost productivity, improve safety, and significantly lower workers' exposure to dangerous conditions on construction sites [5].  
Traditional manual rebar tying is inefficient, labor-intensive, and highly dependent on the worker's skill and physical condition. As a critical task in construction, rebar tying demands precision and sustained high-intensity labor, often resulting in low productivity, inconsistent quality, and safety risks [6, 7]. With recent advances in deep learning and artificial intelligence, neural network-based rebar tying robots have been developed. Theserobots leverage machine vision and image processing to detect rebar intersections and employ robotic arms for precise automatic tying, effectively replacing manual labor in repetitive and strenuous tasks [8,9]. They significantly reduce physical workload, improve consistency, and enhance structural reliability. Currently, rebar tying robots are increasingly adopted in large-scale infrastructure projects such as bridges, high-speed railways, and underground facilities [10]. They help to improve construction efficiency, ensure timely project delivery, and shorten construction timelines. As technology continues to advance and costs decrease, the market potential of these robots is expanding. In the future, rebar tying robots are expected to be integrated with broader construction automation systems, enabling more intelligent workflows and further improving overall construction efficiency and quality [11].  
In rebar tying robotics, vision recognition systems based on deep learning often have limited generalization capability. When used on different construction sites or with various rebar types, these models may fail to maintain high accuracy and can easily overfit [12,13]. Although Time-of-Flight (TOF) camera-based systems offer better generalization, they are highly sensitive to ambient light, which can reduce recognition accuracy [14,15]. Moreover, coordinate transformations between the camera and the robotic arm during operation are complex and can cause significant localization errors, hindering precise tying [16,17]. To address these issues, this paper presents a vision-based detection and path planning system for automated rebar tying. The system captures RGB-D images using a TOF camera and applies depth range filtering and Gaussian smoothing to suppress environmental noise. It then uses the Hough Transform to detect rebar intersections and extract their 2D pixel coordinates. Through extrinsic calibration, these coordinates are converted to 3D positions in the robot arm's coordinate frame, providing accurate spatial data for efficient and precise tying. This study aims to develop and validate an automated framework that reliably detects rebar intersections and generates optimal tying paths under typical site conditions.