# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 1 Introduction
However, their effectiveness depends heavily on clear visibility and prompt recognition. To enhance the accuracy of pedestrian-crossing detection, this study proposes an integrated system combining YOLO-based automatic labeling with a CycleGAN [8] loop-learning framework. This approach not only improves detection precision but also increases the overall system efficiency, enabling faster scalable pedestrian-crossing identification and streamlined model retraining. Our system design offers two advantages:  
* The improved pedestrian-crossing detection system enhances the capabilities of driving assistance technologies by providing more accurate and timely warnings to drivers when approaching pedestrian crossings. This contributes to increased road safety by reducing the risk of pedestrian-related accidents. * The integration of an automatic labeling system significantly reduces the time and manpower required for manual verification, thereby streamlining the annotation process. This study begins with the collection of image and video data capturing pedestrian crossings under diverse conditions. The data were gathered from urban roads surrounding Feng Chia University in Taichung, Taiwan, which represent typical local traffic environments. In addition to varying times of day (e.g., daytime and nighttime), we focused on five specific environmental scenarios: night, rainy day, rainy night, sun glare, and high-beam glare. These five conditions were chosen based on their relevance to real-world driving hazards, particularly those that impair pedestrian-crossing visibility and increase the risk of driver oversight. Due to the limited availability of annotated data under such challenging conditions, daytime scenes--which are easier to capture--were used to train a Cycle-Consistent Generative Adversarial Network (CycleGAN) to generate synthetic images corresponding to these five scenarios. This augmentation technique expands the dataset and improves the model's generalizability. The pedestrian-crossing detection model was trained using YOLO [9; 10; 11] on a combination of real and synthetic datasets. To manage and scale the storage of both raw and annotated images, we utilized a cloud-based infrastructure provided by Amazon Web Services (AWS). A dedicated automated labeling module was developed to leverage the YOLO detection model for large volumes of new images, significantly reducing the need for manual annotation. While limited human verification remains necessary to eliminate false positives, the system is designed to support continuous improvement. Through this iterative loop of data collection, model training, and system deployment, the pedestrian-crossing detection framework is continuously optimized, leading to progressively higher accuracy and reliability in real-world applications. The remainder of this paper is organized as follows: Section 2 provides an overview of the existing research and relevant technologies. Section 3 outlines the system architecture and methodologies adopted in this study. Section 4 details the data collection process and implementation methods used for pedestrian-crossing detection. Section 5 presents the experimental results, including an evaluation of different situational datasets and the impact of incorporating generated images into the training process. Section 6 concludes the study and highlights potential directions for future research.