# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 1 Introduction  
In recent years, the rapid advancement in Internet of Things (IoT) and Artificial Intelligence (AI) technologies has given rise to a wide range of cross-disciplinary innovations. The IoT enables interconnectivity among physical objects via the Internet, allowing devices to sense, collect, and exchange data, as well as to perform context-aware actions. This paradigm shift has accelerated the digital transformation of the physical world, leading to applications across multiple sectors, such as smart manufacturing, smart cities, healthcare, and intelligent transportation systems. Concurrently, AI technologies have been extensively adopted across diverse domains. Among the most prominent applications are cybersecurity and data management, where AI is utilized to detect anomalies, reinforce security architectures, and facilitate intelligent risk assessment. In the medicalfield, AI-powered image analysis and diagnostic tools have significantly improved both diagnostic accuracy and operational efficiency. Furthermore, AI continues to demonstrate substantial value in sectors such as digital marketing, fraud detection, autonomous driving, and governmental surveillance. According to IoT Analytics [1], the global number of connected IoT devices reached 16.6 billion by the end of 2023, marking a 15% increase compared to 2022, with projections estimating growth to 40 billion devices by 2030. Despite these technological advancements, road safety remains a significant concern, particularly in Taiwan, where traffic accidents have dramatically increased by 130%, from 170,127 cases in 2008 to 393,918 cases in 2024 [2]. This surge is largely due to human factors, including driver negligence and delayed responses to road indicators. While traffic signs and road markings are designed for optimal visibility, their effectiveness can be compromised in low-visibility conditions, emphasizing the urgent need for advanced assistive systems. Addressing these challenges, rapid developments in IoT and image recognition technologies have significantly enhanced intelligent transportation systems (ITSs) in smart cities worldwide, substantially improving road safety, traffic efficiency, and sustainability [3]. For instance, [PERSON] et al. developed the CGADNet model [4], a lightweight YOLOv8-based deep learning network specifically tailored for efficient real-time detection of pedestrian crosswalks and guide arrows in complex urban environments. Similarly, [PERSON] et al. proposed the CDNet model [5], which is based on YOLOv5, while [PERSON] et al. introduced X-CDNet [6], an improved model based on YOLOX. Both models were designed to provide robust real-time object detection capabilities, thereby contributing to the advancement of intelligent transportation systems. Additionally, [PERSON] and [PERSON] introduced the SmartDENM system [7], integrating YOLO-based object detection with Vehicle-to-Everything (V2X) communications through Roadside Units (RSUs), enabling real-time decentralized environmental notification messages (DENMs) to prompt immediate safety actions like speed reduction or lane changes. Furthermore, technologies such as vehicular ad hoc networks (VANETs), Intelligent Traffic Lights (ITLs), Virtual Traffic Lights (VTLs), and Mobility Prediction methods further highlight the trajectory toward optimized traffic management, urban sustainability, and significant environmental impact reduction. In contrast, road surface markings--such as lane lines and pedestrian crossings--are inherently more prone to occlusion by vehicles or degradation due to adverse environmental conditions, including poor lighting, nighttime driving, and inclement weather. These limitations can impair driver perception and increase the risk of traffic incidents. In scenarios where intersections lack traffic signs or signals, road markings often serve as the primary means of traffic control. Among these, pedestrian crossings are especially vital for safeguarding pedestrian rights-of-way.