## 2 Related Work  
Object detection algorithms are often used in intelligent transportation, automatic driving, medical imaging, and other scenarios. The object detection algorithm based on deep learning is more and more favored by researchers because of its strong representation ability, flexible network structure, automatic feature learning, and large-scale data processing. The main representative methods of object detection frameworks based on deep learning are Faster R-CNN, YOLO, and SSD. Crosswalks and guide arrows can provide important auxiliary functions for autonomous driving safety, which can help the automatic car better understand the road signs and make prescribed actions, such as slowing down and changing the direction of travel. Although the current automatic driving technology is becoming more and more mature, there are still problems such as the poor real-time performance and detection errors. There are some problems in target detection tasks, such as the poor detection effect of small-sample data training and the difficult deployment of large models of edge equipment. Although transfer learning can be used to train and fine-tune the model, the detection accuracy and speed need to be further improved. The model is difficult to deploy on edge devices with limited computing resources because of its massive parameters, space, and computing resources. The research literature on crosswalks and guide arrows in recent years is presented in Table 1. The literature listed in Table 1 is classified according to traditional methods and deep learning methods, and the characteristics of these methods are summarized. [PERSON] [37] proposed a crosswalk detection algorithm based on equidistant straight lines, which mainly adopts segmented edges to realize recognition. [PERSON] et al. [38] proposed a method based on bipolar features. [PERSON] et al. [39] proposed a lane-pointing arrow recognition algorithm based on multi-class SVM. [PERSON] et al. [40] proposed a curvilinear-based prototype fitting a real-time detection algorithm for pointing arrow marks. [PERSON] et al. [41] adopted the method of image recognition to realize arrow detection and the recognition of urban roads. [PERSON] et al. [42] proposed a road sign extraction algorithm based on mobile laser scanning (MLS) data. The feature of this algorithm is that road marks are scanned by a moving laser to obtain road sign data, and then the road sign data are analyzed to realize road sign detection. The algorithm is easy to deploy and implement but is inefficient. To make up for the efficiency problem of the MLS algorithm, [PERSON] et al. [43] proposed a method based on mobile radar data scanning in 2015 to realize rapid road sign detection. In addition, [PERSON] et al. [44] proposed a sidewalk detection algorithm based on MSER and ERANSAC, and [PERSON] et al. [45] proposed a zebra crossing recognition method based on block Hough transform under natural scenes. [PERSON] et al. [46] proposed sidewalk fringe detection based on gradient similarity tags, which mainly realizes sidewalk detection through the recognition of the stripe blocks in the crosswalk. References [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59] present the deep learning approach. Through the analysis of the research literature on pedestrian crossing and guide arrow recognition detection, we found that deep learning algorithms have significant advantages in the accuracy and recognition speed compared with traditional target recognition algorithms, and traditional recognition algorithms have greater flexibility in the deployment of edge computing hardware. Despite the significant achievements of the methods proposed in the research literature, these approaches still have limitations, particularly in adapting to complex scenarios. They are easily influenced by changes in lighting, shadows, and obstructions, leading to unstable detection accuracy. A significant challenge lies in the difficulty of deploying on-edge devices, rendering them incapable of meeting real-time requirements.