# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 5 Experimental Results
### The Performance in Different Testing-Set Situations  
Before incorporating the generated pedestrian-crossing images into the training process, we first evaluated the performance of a model trained solely on real pedestrian-crossing images under various real-world testing conditions. The goal of this preliminary experiment is to assess whether incorporating images from different environmental scenarios is necessary to improve dataset diversity and detection accuracy. Since daytime images are the most readily available and abundant in our dataset, we selected them as the fixed training set for this experiment. The model is trained exclusively on real daytime images, and its performance is benchmarked against a testing set also composed of real daytime images. To evaluate the model's generalization ability, additional testing is conducted on real pedestrian-crossing images captured under five distinct conditions: night, rainy day, rainy night, sun glare, and high-beam glare. To ensure a fair comparison across all the scenarios, we controlled the dataset size by maintaining a consistent 8:2 ratio between the training and testing subsets.  
Accordingly, the real daytime dataset and the datasets from each of the five environmental conditions were proportionally partitioned to match this ratio. We then conducted a series of evaluations by adjusting the Intersection over Union (IoU) threshold to analyze the F1-score variations. In object detection tasks, performance is typically assessed starting from an IoU threshold of 0.5, incremented by 0.05 up to 0.95. As shown in Figure 9, the F1-score exhibits a consistent downward trend as the IoU threshold increases across all six environmental conditions, which is expected due to the stricter overlap requirement. At IoU = 0.5, high F1-scores were observed in most scenarios (e.g., 0.96 for night, 0.94 for sunlight glare, and 0.91 for day). However, as the threshold rises to 0.8 and above, the decline becomes more pronounced--particularly under challenging lighting conditions. For instance, in the glare from high-beam scenarios, the F1-score drops from 0.87 (IoU = 0.5) to just 0.06 at IoU = 0.8, eventually approaching zero at IoU = 0.9. This indicates reduced boundary precision and highlights the vulnerability of detection performance under intense artificial lighting.  
In contrast, scenarios such as day, night, and rainy night demonstrate more gradual declines in F1-score, suggesting relatively stable spatial prediction performance. Nevertheless, all the non-daytime conditions--including rainy day, rainy night, and sunlight glare--consistently underperform the daytime baseline, even at lower thresholds. These results quantitatively confirm that detection performance is highly sensitive to environmental variability. Overall, the findings underscore that training exclusively on daytime imagesis insufficient for achieving robust detection. It is essential to include diverse real-world conditions--such as low light, rain, and glare--during training to enhance generalization and reliability across different environments.