# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 6 Conclusions  
This study presented a pedestrian-crossing detection system that integrates cyclic learning, CycleGAN-based data augmentation, and cloud-based automation to improve detection accuracy under varying environmental conditions. Real-world images were collected and annotated across diverse scenarios--including day, night, rain, and glare--and augmented with synthetic images to expand the dataset coverage. A YOLO-based detection model trained on this enriched dataset exhibited improved performance, particularly under visually challenging or underrepresented conditions. To reduce manual effort and streamline data preparation, an automated labeling module was incorporated into the loop-learning framework. While not quantitatively assessed, the module significantly reduced the need for manual annotations during iterative training, enabling synthetic images to be automatically labeled and reused in successive rounds. The experimental results revealed considerable variation in model performance across different environmental conditions.  
Figure 10: F1-score comparison of various datasets.  
As expected, daytime scenes yielded the highest F1-scores, while rain, glare, and nighttime conditions posed greater challenges. Notably, adding 500 CycleGAN-generated images to 500 real samples improved the average F1-score by 5.7%, with gains exceeding 10% in the most difficult scenarios, such as rainy nights and high-beam glare. These findings validate the effectiveness of combining generative augmentation with automated labeling to address data scarcity and enhance detection robustness.  
Future work will focus on real-time integration between vehicle front-end and cloud-based back-end systems to deliver immediate pedestrian-crossing alerts. While the current evaluation was conducted in an offline environment, the system architecture is designed with real-time deployment in mind. This work demonstrates how the integration of generative models and automated annotation can improve the generalization of detection systems in complex environments. The proposed system lays a foundation for scalable real-time safety applications in intelligent transportation. By improving the visibility and timely recognition of pedestrian crossings, it also supports broader goals in sustainable urban mobility, particularly in enhancing pedestrian safety and reducing traffic-related risks in walkable cities. The intended end users are drivers operating in urban areas with complex or low-visibility conditions. Future extensions may explore integration with smart city infrastructure to further promote pedestrian- and cyclist-friendly mobility strategies.  
Conceptualization, K.-C.W. and C.-R.D.; methodology, K.-C.W. and C.-L.M.; software, K.-C.W.; validation, K.-C.W.; formal analysis, K.-C.W.; investigation, C.-L.M.; resources, C.-R.D. and C.-L.M.; data curation, K.-C.W.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and C.-L.M.; visualization, K.-C.W.; supervision, C.-L.M.; project administration, C.-R.D.; funding acquisition, C.-R.D. All authors have read and agreed to the published version of the manuscript. This research was supported by the Ministry of Science and Technology under grant number NSTC 110-2221-E-035-007-.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.