# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 4 Experiments and Analysis
### Ablation Study  
To assess the effectiveness of the GCBAM-UNet model, we compared it with UNet and VGG19-UNet. As shown in Table 2, our GCBAM-UNet model demonstrated superior segmentation performance, achieving the highest scores in AC, DC, JI, SE, and SP, with values of 92.78%, 86.09%, 66.52%, 90.13%, and 99.21%, respectively.  
Moreover, the computational performance of GCBAM-UNet strikes a balance between accuracy and efficiency, making it practical for deployment in real-time systems. The use of VGG19 as the encoder leverages pre-trained weights, reducing the computational overhead associated with training from scratch. While VGG19 is relatively deep, its streamlined architecture ensures that feature extraction is computationally efficient without unnecessary complexity. The addition of the CBAM enhances the model's precision by focusing on critical features. CBAM can have a minimal additional computational cost as it uses lightweight attention mechanisms. As depicted in Table 2, we can remark that GCBAM-UNet is faster than U-Net and VGG19-UNet since CBAM-UNet achieves early stopping in fewer epochs compared to the other methods, which demonstrates a faster convergence. This indicates that the CBAM mechanism effectively guides the learning process by focusing on the most relevant spatial and channel features, reducing the number of training iterations required to reach optimal performance.  
To evaluate the impact of the CBAM mechanism, we conducted an ablation study, as shown in Table 2. CBAM modules were integrated into each layer of the decoding branch, and the segmentation results were compared between the model with CBAM (GCBAM-UNet) and without CBAM (VGG19-UNet). The integration of CBAM significantly improved the segmentation performance, as evidenced by increases in AC, DC, JI, SE, and SP by 0.97%, 6.45%, 6.4%, 4.93%, and 1.16%, respectively.  
Segmenting sun glare in images presents significant challenges due to the difficulty in accurately distinguishing glare from other image components. The segmentation results are illustrated in Figure 4. As depicted, the U-Net and VGG19-UNet models exhibit  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **AC** & **DC** & **JI** & **SE** & **SP** & **Training** & **Testing** \\ \hline U-Net & 91.19 & 70.59 & 46.62 & 76.69 & 97.86 & 42 min & 1 s \\ VGG19-UNet & 91.81 & 79.64 & 60.12 & 85.20 & 98.05 & 40 min & 1 s \\ GCBAM-UNet & 92.78 & 86.09 & 66.52 & 90.13 & 99.21 & 39 min & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study performance (in %).  
considerable misinterpretation in sun glare segmentation, with numerous errors in differentiating between glare pixels and the background. In contrast, the GCBAM-UNet model demonstrates superior performance, providing more accurate segmentation of sun glare.  
Figure 4: Segmentation outcomes of our proposed model compared to U-Net and VGG19-UNet.  
Our proposed approach achieves more precise segmentation, indicating its suitability for ADAS. It performs particularly well in handling irregular edges and small areas. However, some regions may still be mis-segmented especially when glare has soft edges or blends gradually into the surrounding image. This can lead to imprecise segmentation boundaries.