## 1 Introduction
Therefore, designing and implementing an object detection algorithm for high-definition images, and enhancing the object detection precision and efficiency while ensuring real-time performance, constitute challenging tasks. Figure 1 illustrates the scenario of object detection in urban road crosswalks and lane markings within complex scenes, encompassing varied weather conditions, diverse lighting intensities, and different detection scenarios. Handling these complex real-world scenarios with traditional algorithms falls short of our expectations. Therefore, there is an urgent need to design a more efficient and robust detection algorithm. Among the milestones of object detection [11], the main components include the following: VJ Det [12; 13]; HOG Det [14]; DPM [15; 16; 17]; RCNN [18]; SPPNet [19]; Fast RCNN [20]; Faster RCNN [21]; Mask R-CNN [22]; YOLO [23; 24; 25; 26; 27; 28]; SSD [29]; FPN [30]; RetinaNet [31]; CornerNet [32]; CenterNet [33]; and DETR [34]. Undeniably, the flourishing development of deep learning [35] technology has significantly propelled the advancement of object detection, achieving noteworthy breakthroughs and catapulting it into an unprecedented research hotspot. Despite the satisfactory performance achieved through carefully optimized deep neural networks, their escalating complexity poses substantial challenges for edge deployment [36]. This study aimed to achieve fast and accurate crosswalk and guide arrow detection on the limited edge computing device Jetson Orin Nano. We propose a lightweight object detection method to address the challenges of the low detection accuracy and efficiency in the detection of urban road crosswalks and guide arrows in autonomous driving. In this paper, we introduce a lightweight algorithm by optimizing YOLOv8, enabling the effective detection of crosswalks and guide arrows in high-definition vehicle-mounted camera images. First, we optimized the visual neural network by introducing the C2f_Van module, significantly improving the model size and detection speed. Secondly, to construct a more lightweight deep learning neural network, we employed depth-separable convolution. Then, we proposed the efficient and lightweight FasterDetect detection head to further optimize the model structure and enhance the detection speed. Finally, to improve the algorithm's target positioning and prediction accuracy, we use WIoU\({}_{v3}\) instead of the CIoU loss. The main contributions of this work can be summarized as follows:  
* In this study, we adopted the minimalist design concept and used VanillaBlock to build an innovative C2f_Van module, effectively reducing the complexity of the model. To reduce the network parameters, deep separable convolutional networks were designed to achieve lightweight models;
* To reduce the computation of the network and improve the detection speed of the model, a PConv-based fast detection header called FasterDetect was designed to replace the detection header in the Yolov8 network. Such a mechanism makes it easy for models to be deployed and run on edge computing devices. In addition, a boundary box regression loss (WIoU\({}_{v3}\)) function based on a dynamic non-monotone focusing mechanism was designed to improve the recognition accuracy and generalization ability of the model;
* Through collection and meticulous annotation, the CADG database for the detection of crosswalks and guidance arrows on urban roads was made. The dataset contains 24,560 high-resolution images of crosswalks and directional arrows on urban roads in real, complex scenarios, including rain, fog, night, dim light, and broken sidewalk markings. Through data training in different scenarios, the adaptability of the model to complex environment detection was realized. The rest of this paper is organized as follows. Section 2 provides an introduction to related research work. Section 3 outlines the methodology, detailing the model architecture proposed and the methods employed. The dataset description, experimental procedures, and results are expounded in Section 4.