## 3 Results
### Model Training
#### 3.2.2 Adaptive MobileNetV2  
To preserve the general feature extraction capabilities of the pretrained MobileNetV2, the first 116 layers were frozen during training. This ensures that low-level and mid-level visual features, which are transferable across tasks, remain unchanged. The remaining layers were fine-tuned to adapt the model to the specific characteristics of the target dataset. The training results showed that the red light achieved very high accuracy across all pre-processing methods. Both true positive (TP) and true negative (TN) rates remained at 1 in every case, with no errors from false negatives (FN) or false positives (FP), except when using CLAHE. In that case, FP slightly increased to 0.0077, causing TN to drop to 0.99 as shown in Figure 14. However, this is still considered an excellent result and does not significantly affect the model's overall performance. Therefore, it can be concluded that red-light data is highly stable regardless of the pre-processing technique applied. For the green light, all image pre-processing techniques result in TP values ranging from 0.97 to 1.0, indicating good overall performance. However, HE and CLAHE slightly reduce the TP values and increase the FN values compared to the original and normalization methods. Specifically, CLAHE yields the lowest TP at 0.97, with the highest FN of 0.031, while normalization achieves a TP of 1.0 and FP of 0, which outperforms all other methods. As shown in Figure 15, the best performance under green light conditions is achieved with normalization, followed by the original, with HE and CLAHE performing the worst. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Light Color** & **Pre-Processing** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{3}{*}{Red} & Original & 99.60 & 99.63 & 99.57 & 99.60 \\  & Normalization & 99.20 & 99.16 & 99.23 & 99.19 \\  & HE & **99.80** & **99.80** & **99.80** & **99.80** \\  & CLAHE & **99.80** & **99.80** & **99.80** & **99.80** \\ \hline \multirow{3}{*}{Green} & Original & 97.82 & 97.83 & **98.80** & 97.82 \\  & Normalization & 97.23 & 97.36 & 97.16 & 97.22 \\  & HE & **98.22** & **98.21** & 98.22 & **98.22** \\  & CLAHE & 97.23 & 97.24 & 97.19 & 97.21 \\ \hline \multirow{3}{*}{Blue} & Original & **99.55** & **99.55** & **99.56** & **99.55** \\  & Normalization & 97.33 & 97.34 & 97.32 & 97.33 \\  & HE & 96.21 & 96.21 & 96.29 & 96.21 \\  & CLAHE & 97.77 & 97.89 & 97.71 & 97.77 \\ \hline \multirow{3}{*}{White} & Original & **98.34** & **98.34** & **98.34** & **98.34** \\  & Normalization & 97.67 & 97.68 & 97.69 & 97.67 \\ \cline{1-1}  & HE & 94.68 & 94.68 & 94.65 & 94.67 \\ \cline{1-1}  & CLAHE & 94.02 & 94.02 & 94.00 & 94.01 \\ \hline \hline \end{tabular} Bold numbers represent the highest accuracy for each light source.  
\end{table} 
Table 3: Output of the experiments with MobileNetV2. Figure 14: Confusion matrix of adaptive MobileNetV2 under red light. (**a**) Original image, (**b**) normalization, (**c**) HE, and (**d**) CLAHE. Figure 15: Confusion matrix of adaptive MobileNetV2 under green light. (**a**) Original image, (**b**) normalization, (**c**) HE, and (**d**) CLAHE. For the blue light, both the original and HE pre-processing methods result in excellent performance, with TP = 1 and FN = 0 in both cases. In particular, blue HE achieves the best result, with FP = 0, while the original method has a slightly higher FP of 0.0043, which is still acceptable. In contrast, the normalization method produces the highest FP value of 0.035 and the lowest TN of 0.97 among all cases, indicating a significant drop in performance. Blue CLAHE shows similar performance to the original and HE methods but still introduces a small FP. As shown in Figure 16, the best methods for blue light data are HE and the original, while normalization should be avoided. For the white light, most pre-processing methods maintain high performance, with TP = 1 in nearly all cases. Only CLAHE slightly reduces the TP to 0.99.