## 1 Introduction  
With the rapid development of technology, autonomous driving [1, 2] has entered a new era, integrating edge computing [3], machine vision [4], and 5G technology [5, 6] to bring unprecedented advancements to the driving experience. This comprehensive innovation not only promotes the integration of communications, computer vision, and intelligent transportation technologies but also the rapid development of autonomous driving technology. Through advanced real-time detection technology, vehicles can obtain crucial information such as pedestrian crosswalks and guide arrows in real time, which provides an important decision-making basis for Automatic Driving Systems (ADSs) [7] and Advanced Driving Assistance Systems (ADASs) [8]. In recent years, Intelligent Traffic Management Systems (ITMSs) [9] have become increasingly complex and intelligent, drawing widespread attention to safety issues at pedestrian crosswalks. Collisions between pedestrians and vehicles occur from time to time, resulting in significant personal injuries, fatalities, and substantial societal and economic losses. According to statistics from the National Bureau of Statistics of the People's Republicof China, there were 139,412 recorded cases of automotive traffic accidents nationwide in 2017. Surprisingly, in 2021, this number rose dramatically to 171,941 incidents. Additionally, according to the 2017 statistical data from the Traffic Management Bureau of the Ministry of Public Security of the People's Republic of China, there have been 14,000 cases of traffic accidents involving motor vehicles and pedestrians at crosswalks in the past three years nationwide. These accidents resulted in 3898 fatalities, with 90% attributed to the non-compliance of motor vehicles with regulations. This is a staggering and alarming statistic. Using technology to prevent sidewalk tragedies is a very urgent task. The discerning investigation into the efficacious detection of pedestrian crosswalks and guide arrows on urban thoroughfares, coupled with the prompt issuance of cautionary signals and the prevention of traffic accidents, has become one of the crucial research topics in the field of intelligent traffic safety. Simultaneously, the increased demand for environmental information in advanced-level driving has led to the evolution of in-vehicle cameras toward achieving 'high definition'. Serving as the 'eyes' of autonomous driving vehicles, in-vehicle cameras act as the visual perception sensors essential for constructing ADSs and ADASs. Leveraging the detection information derived from image data captured by high-definition in-vehicle cameras (as illustrated in Figure 1), the accurate detection and in-depth analysis of crosswalks and guide arrows can be achieved, providing pivotal information for the decision-making processes of both ADSs and ADASs. Figure 1: Detecting and analyzing crosswalks and guide arrows in ever-changing complex scenes based on high-definition images: S1: clearly visible; S2: sunlight glare; S3: dim light; S4: crossroads; C1: overcast conditions; C2: gloomy turning crosswalk; C3: pedestrian crowd; C4: vehicle obstruction; N1: nighttime visibility; N2: dark night envelops; N3: night with lingering raindrops; N4: elevated road guidance arrow; R1: rain reflection; R2: wiper blocking; R3: crosswalk markings eroded by rain; R4: light source interference; F1: fog conditions; F2: dense fog; F3: water accumulation obstruction; F4: partial loss. However, with the enhancement of the image resolution input into neural networks [10], high-definition images exhibit increased detail and complexity and larger data volumes, resulting in heightened computational complexity and augmented resource requirements. Constrained by the limitations of edge computing capabilities, the required inference time experiences a significant increase, posing challenges to meeting the real-time detection demands within autonomous driving scenarios.