# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 5 Experimental Results
### F1-Score Enhancement via CycleGAN-Augmented Training Sets  
As previously established, environmental variations significantly impact the performance of pedestrian-crossing detection. To achieve reliable detection across diverse scenarios, it is necessary to increase the quantity and diversity of training data corre \begin{table}
\begin{tabular}{l l l l l} \hline
**Situation** & **200 Epochs** & **FID** & **5000 Imgs** & **FID** \\ \hline \multirow{8}{*}{Day to Night} & 1000 imgs & 133.55 & 50 epochs & 152.46 \\  & 2000 imgs & 107.75 & 100 epochs & 153.77 \\  & 3000 imgs & 119.06 & 150 epochs & 105.23 \\  & 4000 imgs & 98.79 & 200 epochs & 114.82 \\  & 5000 imgs & 114.82 & & \\ \hline \multirow{8}{*}{Day to Rainy Day} & 300 imgs & 206.47 & 50 epochs & 209.66 \\  & 600 imgs & 182.00 & 100 epochs & 184.41 \\  & 900 imgs & 147.84 & 150 epochs & 142.21 \\  & 1200 imgs & 126.96 & 200 epochs & 142.28 \\  & 1500 imgs & 142.28 & & \\ \hline \multirow{8}{*}{Day to Rainy Night} & 200 imgs & 312.61 & 50 epochs & 299.8 \\  & 400 imgs & 217.8 & 100 epochs & 223.55 \\  & 600 imgs & 203.86 & 150 epochs & 199.34 \\  & 800 imgs & 182.22 & 200 epochs & 179.96 \\  & 1000 imgs & 179.96 & & \\ \hline \multirow{8}{*}{Day to Sun Glare} & 200 imgs & 218.72 & 50 epochs & 331.19 \\  & 400 imgs & 168.88 & 100 epochs & 137.75 \\ \cline{1-1}  & 600 imgs & 120.87 & 150 epochs & 129.48 \\ \cline{1-1}  & 800 imgs & 134.52 & 200 epochs & 117.92 \\ \cline{1-1}  & 1000 imgs & 117.92 & & \\ \hline \multirow{8}{*}{Day to High-Beam Glare} & 200 imgs & 298.60 & 50 epochs & 169.37 \\ \cline{1-1}  & 400 imgs & 249.17 & 100 epochs & 133.56 \\ \cline{1-1}  & 600 imgs & 197.57 & 150 epochs & 167.12 \\ \cline{1-1}  & 800 imgs & 141.78 & 200 epochs & 133.78 \\ \cline{1-1}  & 1000 imgs & 133.78 & & \\ \hline \end{tabular}
\end{table} 
Table 3: FID scores for different situations under training volume and epoch variations. sponding to these conditions. However, acquiring such data is inherently challenging as it requires capturing images during specific weather events or lighting conditions that are infrequent or difficult to replicate. To address this limitation, we employ CycleGAN to generate synthetic images that simulate these rare or hard-to-capture scenarios for the purpose of data augmentation. This experiment aims to evaluate the effect of different quantities of generated images on the model's detection performance, measured using the F1-score. For each of the five scenarios (night, rainy day, rainy night, sun glare, and high-beam glare), we select the generator models that achieved the lowest FID scores in the previous experiment, ensuring the highest image quality. The baseline training set consists of 500 real images per condition. We then incrementally add 250, 500, 750, and 1000 generated images to train new detection models. For comparison, an additional model is trained using 1000 real images. Each trained model is evaluated using 100 real independent testing images from the corresponding condition, using an IoU threshold of 0.5. Additionally, we construct dependent testing sets that share some distributional similarity with the training data, also evaluated at IoU = 0.5. The F1-score is chosen as the primary evaluation metric as it provides a balanced view of both precision and recall, making it particularly suitable for comparative analysis in this context. The detailed results are summarized in Table 4, which reports F1-scores across five environmental conditions under varying training configurations. Each scenario includes two rows: the first corresponds to the independent testing set, while the second represents the dependent testing set. Across all the scenarios, the models trained with a combination of real and generated images consistently outperform those trained on 500 real images alone. Notably, the configuration of 500 real + 500 generated images yields a significant performance gain and, in many cases, approaches the F1-score of the 1000 real-only configuration.