# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 5 Experimental Results
### Comparison of FID Scores of Generated Images  
The Frechet Inception Distance (FID) score is a widely adopted metric for evaluating the quality of images generated by GANs. Proposed by [PERSON] et al. in 2017 [40], FID quantifies the similarity between two sets of images based on the statistical distance between their feature representations, which is extracted using the Inception v3 image classification network. Specifically, it compares the mean and covariance of real and generated image feature distributions. A lower FID score indicates higher similarity between the generated and real images, with an ideal score of 0.0, signifying perfect alignment--although this is rarely achievable in practice when working with synthetic image generation.  
In our experiment, FID scores were computed using the output from the final pooling layer of Inception v3, with a feature dimension of 2048. We trained multiple CycleGAN models using varying amounts of real images and training epochs to simulate five environmental conditions: night, rainy day, rainy night, sun glare, and high-beam glare. Each trained model was then used to generate 1000 images per condition, which were compared against corresponding real-image sets from the same condition using FID. The goal of this evaluation is to quantify the visual closeness of the synthetic images to real-world counterparts under different training configurations.  
The FID scores obtained across different data volumes and epoch counts are summarized in Table 3. The results demonstrate that increasing both the number of training images and the number of training epochs generally leads to improved FID scores. For example, in the day to night scenario, the FID decreases from 133.55 (with 1000 training images) to 98.79 when trained with 4000 images for 200 epochs--representing the best-performing configuration overall. Similar downward trends are observed in the other conditions, particularly those involving glare or low-light settings. Nevertheless, the improvements tend to plateau beyond 200 epochs, and, in a few cases, excessive training data or longer training durations result in slightly increased FID scores, likely due to model instability or overfitting. These findings suggest the need for careful tuning of training volume and duration to achieve optimal generative performance.  
Figure 9: Comparison of F1-score curves of six situations.  
While these FID scores exceed the high-quality thresholds typically reported in natural image benchmarks, our visual inspection and the detection performance results presented in Section 5.3 suggest that synthetic images with FID values below 200 still retain sufficient structural and contextual realism to support effective model training. These findings indicate that, for task-specific synthetic image generation--particularly under challenging lighting conditions--a higher FID threshold may still be acceptable for practical use. In our case, images with FID scores below 200 were deemed realistic enough to be incorporated into the pedestrian-crossing detection training pipeline. Among the evaluated cases, the best-performing model achieved an FID score of 98.79, indicating high visual fidelity to real images--albeit requiring substantial training resources.  
In this study, FID was used as a theoretical basis for selecting the most visually realistic CycleGAN models under each environmental condition. The models yielding the lowest FID scores were chosen to generate synthetic images, which were then incorporated into the detection model training in Section 5.3. However, we note that no direct experimental comparison was conducted to evaluate how different FID levels affect F1-score outcomes. Therefore, while the selection of lower-FID models is consistent with the common practice in generative model evaluation, the impact of FID on downstream detection performance remains a potential direction for future investigation.