# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 2 Related Work  
Traditional pedestrian-crossing detection methods primarily rely on the characteristic black-and-white-striped patterns of zebra crossings. The early studies often adopted these visual cues as the foundation for algorithm selection and system design [12]. [PERSON] et al. [13] proposed a method based on bipolarity-based segmentation and projective-invariant-based recognition. Their approach verified the bipolarity of each image block and used a voting mechanism grounded in projective invariants to guide image binarization. [PERSON] et al. [14] employed the Hough Transform to extract pedestrian-crossing lines from surveillance footage, enabling both location and directional analyses. Additionally, their method assessed the degradation of pedestrian crossings, providing valuable insights for road safety management. [PERSON] et al. [15] applied Speeded-Up Robust Features (SURFs) to extract distinct striped patterns for pedestrian-crossing detection. Despite its high computational cost at increased resolutions, SURFs demonstrated strong accuracy in feature extraction. With the advent of deep learning and object detection technologies, traffic information retrieval has significantly improved, surpassing traditional hand-crafted feature-based techniques. [PERSON] et al. [16] applied Fast R-CNN for vehicle detection in aerial imagery, using VGG-16 as the backbone network. Their approach divided input images into multiple regions and predicted object presence with confidence scores, effectively eliminating irrelevant background regions such as rural landscapes, thereby reducing computational overhead. [PERSON] et al. [17] developed a traffic sign detection algorithm based on a deep convolutional neural network (CNN) utilizing a region proposal network (RPN). Their system, capable of real-time performance and achieving over 99% detection accuracy, could identify Chinese traffic signs from onboard cameras and dashcams, contributing to comprehensive traffic sign datasets. In the domain of pedestrian-crossing detection, [PERSON] et al. [18] integrated environmental feature vectors with YOLO-based pedestrian detection to identify both crossings and waiting areas. Their hybrid system combined deep learning for pedestrian identification with traditional image processing techniques for crossing recognition. [PERSON] et al. [19] adopted the YOLO architecture, fine-tuning it on the COCO dataset to enhance pedestrian-crossing detection. These studies collectively highlight the shift from classical vision-based techniques to deep-learning-based approaches, offering improvements in detection accuracy, robustness, and real-time performance across various traffic monitoring tasks. In recent years, the YOLO architecture has been extensively applied to pedestrian-crossing image-detection tasks [4; 5; 6]. Data collection efforts have typically focused on acquiring images from real-world road scenes or publicly available online image repositories. Data augmentation methods frequently involve synthetic scene simulations, such as foggy conditions; however, these approaches often overlook whether the simulated scenarios adequately resemble real-world conditions. The method proposed in this study leverages a limited set of real-world images captured under specific challenging conditions to generate synthetic images from standard daytime images, thereby enriching the training dataset and balancing class distributions. This approach ultimately enhances the model's F1-scores across various scenarios. Table 1 presents a comparison of the data collection and augmentation methods between our proposed approach and those in [4; 5; 6]. While deep learning methods are powerful, their effectiveness largely depends on the availability of well-annotated large-scale datasets. Manual data labeling, however, remains time-consuming, repetitive, and labor-intensive. To address this bottleneck, several