# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 2 Related Work
### Approaches-Based Deep Learning  
[PERSON] et al. [30] detected the sun glare in panorama images provided by Google Street View (GSV). These street-level images are very similar to the driver's conditions and offer detailed views of road obstructions, enabling the provision of precise information about these obstacles. The authors used a pyramid scene parsing convolutional neural network (PSPNet) to segment the GSV images. By utilizing image segmentation results along with a geometrical model of the sun and drivers, sunlight glare occurrences were predicted while considering all obstructions, such as buildings and trees. This approach produced a spatio-temporal distribution of sun glare occurrences, helping to manage the effects of sun glare while driving. [PERSON] et al. [31] proposed a complementary solution that aimed to enhance the robustness of automated driving systems in handling sun glare. First, the authors used traditional image processing techniques such as color conversion, geometric kernels, adaptive threshold, and blob detection. Second, they employed CNN with five convolution blocks to learn the global context of the image. Finally, they created and publicly shared a new dataset called \"WoodScape\" [32]. [PERSON] and [PERSON] [33] acquired a dataset for sun glare detection by different cameras. The dataset consisted of sun glare images with their corresponding binary mask that depicted the presence of glare in the image. Using this dataset, the authors adapted the U-Net deep learning architecture to create an optimized glare segmentation network. This network extracted features from multiple image representations across different branches and integrated them to detect glare. Finally, they tested various image representations for glare segmentation within the adapted U-Net deep semantic segmentation pipeline. [PERSON] et al. [34] proposed a self-supervised sun glare detection approach for autonomous driving systems. First, the authors introduced a method to generate glare-affected images using a combination of physical models and Generative Adversarial Networks (GANs) to handle the problem of data collection and annotation. After that, they trained the detection model in a self-supervised manner, using synthetic glare data to improve performance and generalization. [PERSON] et al. [35] proposed a new approach to water sun glint detection in Unmanned aerial vehicles (UAV) called \"Res_AUNet\". The authors integrated the CBAM into the U-Net architecture to extract the sun glint features in spatial and channel dimensions. Also, they added residual structures and reduced the number of convolutional kernels to prevent overfitting. Finally, the approach was tested on the WSGD dataset, achieving an accuracy of 98.02%, an F1-score of 83.67%, and an IOU of 74.73%. The previous existing methods show some limitations in solving sun glare. Many approaches struggle to accurately detect sun glare due to dynamic changes in sunlight intensity, angle, and reflection. Also, they may work well in controlled environments but fail in real-world scenarios with diverse backgrounds, angles, and lighting conditions. In fact, these approaches often misinterpret other light sources such as headlights or streetlights as glare, leading to a high rate of false positives. Moreover, sun glare detection suffers from limited datasets, which can cause poor generalization to new scenarios or regions where lighting conditions differ, especially since the sun glare intensity varies throughout the day, making it challenging for some methods to perform well under extreme sunlight conditions, like dawn and dusk. Recently, various approaches have introduced numerous modifications to enhance U-Net's performance and adaptability across diverse tasks [36; 37; 38; 39]. [PERSON] et al. [37] incorporated a VGG16 model into the encoder of the SegNet architecture, leveraging transfer learning for improved feature extraction, and added a CBAM module to enhance the decoder's capability in generating fine-grained segmentation. Similarly, [PERSON] et al.