## 3 Overall System
### Visual Recognition System
#### 3.1.3 Intersection Point Recognition System  
The raw RGB-D image obtained from the camera, after processing by the image preprocessing system and the robust depth data augmentation system, results in a binarized image. This processed image is then used in conjunction with the intersection point recognition system for rebar intersection detection.The workflow of the intersection point detection system is shown in Figure 5.  
After binarization, the image needs to undergo thinning. The purpose of the image thinning algorithm is to progressively reduce the binary image (such as the lines in the rebar image) until it generates a skeleton that is one pixel wide. Specifically, the algorithm iteratively removes pixels that meet certain conditions, simplifying the image into a skeleton for rebar intersection point detection using the Hough Transform. The main objectives of thinning are as follows:  
* Reduce redundant information: By thinning the image, the lines in the original image are simplified to a one-pixel-wide skeleton, eliminating the thickness and redundant information of the lines. This ensures that no extra detection results are generated due to line width during the Hough Transform, thereby improving computational efficiency. * Improve line detection accuracy: In the unthinned image, wide lines can lead to multiple similar Hough parameters being generated for different pixels, increasing unnecessary interference. The thinned skeleton retains only the centerline, making the representation of each line in Hough space more consistent, which enhances the accuracy of line detection. * Enhance the reliability of intersection point detection: After skeletonization, the intersections become clearer, especially in complex structures where overlapping lines do not cause misjudgments due to thickness. This makes the intersection points detected by the Hough Transform more reliable and consistent with the actual structure. * Reduce computational overhead: The Hough Transform typically calculates every pixel in the image, but the thinned image contains fewer pixels, significantly reducing  
Figure 5: Workflow diagram of the rebar intersection point detection system. The process starts from a binarized image and involves skeletonization, pruning, and Hough Transform steps, culminating in the final intersection point detection result on the original RGB image. the computational load. Therefore, skeletonization helps to lower the computational cost of the Hough Transform. Skeletonized images typically preserve the main structural lines of an object; however, the skeletonization process may also produce irrelevant short branches or protrusions. These short branches can be mistakenly detected as independent line segments during the Hough Transform, increasing false positives. To address this, morphological pruning is applied to remove isolated points or short protrusions that are unconnected or connected by only a single pixel. Pruning the skeleton before the Hough Transform improves detection accuracy, reduces false lines, lowers computational complexity, and enhances the clarity and stability of the results. This ensures that the Hough Transform focuses on the main skeleton structure, leading to more reliable line detection. In this method, pruning removes short branches while preserving the endpoints of the main structure to avoid incorrectly truncating line segments near intersections. As a result, the primary structure becomes more distinct, intersection points remain intact and noise is reduced, enabling more accurate and continuous line detection. The pruning process applied in this study is illustrated in Figure 6.  
Subsequently, probabilistic Hough Transform is applied to extract line segments from the pruned image. In the standard Hough Transform, a line can be represented in polar coordinates, as shown in Equation (5).