## 2 Related Research  
The vein segmentation algorithm and the projection correction algorithm are two crucial technologies in auxiliary venipuncture devices. The former involves extracting vessels from dorsal hand images, while the latter addresses the issue of inaccurate projection caused by different equipment angles. In the field of image segmentation, [PERSON] [8] proposed an improved repeated line tracking algorithm for finger vein image segmentation in 2013. The algorithm utilizes vein width in the image to determine the parameters, then uses the improved repeated line tracking method to find the locus space of the finger vein. Finally, the Otsu method is applied to extract the finger vein. To distinguish the vein region on the dorsal part of the hand from the non-vein region, [PERSON]. [9] utilized the kernel fuzzy C-means (KFCM) algorithm for initialization. Furthermore, they designed a defogging algorithm and an edge-fitting term to improve the segmentation process, ultimately achieving better results. In 2012, with the ascent of convolutional networks, artificial intelligence [10] has experienced rapid growth and advancement, and it has gradually found its way into various aspects of human life. [PERSON]. [11] proposed an end-to-end fully convolutional network to handle image segmentation tasks in 2015. The approach utilized various classification networks for downsampling and ultimately yielded precise segmentation results. In the same year, [PERSON] et al. [12] proposed a U-Net for image segmentation. The U-Net consists of an encoder-decoder architecture that combines low- and high-level information, making it suitable for the precise segmentation of medical images. [PERSON] et al. [13] proposed a deep convolutional neural network architecture with nested U-Net by combining global and local loss information to optimize the network, achieving better segmentation results. In 2021, [PERSON] et al. [14] used U-Net with an attention mechanism for dorsal hand vein image segmentation. The algorithm highlighted the feature that had a significant impact on the results and utilized a jump-connected structure for multi-scale feature recognition, thereby enhancing the accuracy of segmentation. The traditional image segmentation methods have limitations in practical applications, which can be greatly affected by grayscale distribution, and are poor at handling noise. Compared with the traditional methods, neural networks can artificially divide the image contours to make labels [15] and specify the features to be extracted [16]. This approach can better handle noise, lighting variations, and other factors, making the neural networks more robust than the traditional methods. In this paper, we propose an approach to image segmentation using neural networks. Specifically, we improve the structure of existing segmentation networks and reduce the parameters for network training, while ensuring segmentation accuracy. Currently, although projection correction algorithms have yielded relatively good results, their application still has some shortcomings. The first venous projector was invented by American medical engineer [PERSON] [5] in 2004. It utilizes a hot mirror to adjust the optical path of the camera and projector to coaxial, eliminating the influence of curved projection and enabling the projected image to coincide with the object. However, this method presents challenges in the precision requirements for the equipment, and coaxial optical path alignment is difficult to achieve. In 2013, [PERSON]. [17] designed a venous projector that treats the dorsum of the patient's hand as a planar surface with four labeled points. The projection image is adjusted through a perspective transformation so that the four points in the projection image align with the four points labeled on the hand, facilitating accurate projection onto the dorsum of hand. However, this method requires the calibration of the four points before each venipuncture, which is cumbersome. In 2016, [PERSON] et al.