# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 2 Related Work
(2019) introduced a refined soft pseudo-labeling approach guided by centroid similarity ranking, which enhances label reliability in domain-adaptive vehicle re-identification. Additionally, [PERSON] et al. [PERSON] et al. (2019) incorporated generative adversarial networks (GANs) into semantic segmentation tasks, demonstrating that adversarial learning can capture subtle visual features and improve model generalization in challenging road scenes. Collectively, these advancements underscore the transformative impact of pseudo-labeling in automating the annotation process, accelerating model development, and enhancing performance in intelligent transportation systems. These advancements in AI-assisted data annotation significantly improve the scalability, efficiency, and accuracy of deep learning systems. Future directions may further explore human-AI collaboration for annotation refinement and enhanced adaptability across domains. In addition to data labeling, cyclic learning has emerged as a powerful training strategy in machine learning, emphasizing iterative and feedback-driven processes. Unlike static training protocols, cyclic learning frameworks adjust parameters or data representations in repeated loops to promote better convergence and generalization. The key examples include the following:  
* Cyclical Learning Rate Schedules: Introduced by [PERSON] (2018), this approach oscillates the learning rate within a predefined range during training to prevent convergence to suboptimal local minima, reducing the need for extensive manual tuning while enhancing classification performance. * General Cyclical Training: Also proposed by [PERSON], this broader concept involves cyclically modifying parameters such as weight decay, batch size, data augmentation strategies, or loss functions during the training phase to prevent overfitting and encourage learning of more robust features. * Iterative Reinforcement Learning Loops: Common in reinforcement learning (RL) [8; 31; 32; 33], agents interact with the environment through repeated cycles of action, observation, and policy updates. This self-improving loop enables agents to discover superior strategies over time, often outperforming manually designed policies in complex tasks. Across various disciplines--including engineering design, control systems, and organizational learning--cyclic learning offers adaptive refinement through feedback, enhancing both the robustness and efficiency of systems. In the context of pedestrian-crossing detection, such iterative training strategies can be harnessed to continuously improve model performance via recurring data collection, retraining, and system deployment cycles. Compared with the prior work, our proposed system differs in three key aspects. First, unlike traditional vision-based approaches and deep learning models that rely heavily on large-scale annotated datasets, we introduce a CycleGAN-based loop-learning framework to address data scarcity under visually challenging conditions. Second, while the existing pedestrian-crossing detection systems often focus solely on model performance, we integrate a dual-loop system that includes automated labeling and iterative retraining, enabling continual model refinement with minimal manual intervention. Third, rather than assuming ideal training environments, our method targets five safety-critical environmental conditions--such as glare and rainy night scenes--that are typically underrepresented in prior datasets. These design choices allow our approach to maintain robustness in real-world applications and lay the foundation for future deployment in driver-assistance systems.