## 2 Related Work  
In the process of model-development studies on underwater images, various methods for image restoration examine mathematical models on images in the preprocessing stage, eventually obtaining higher-quality underwater images or evaluating the results by combining pre-trained segmentation and classification methods within any framework of deep learning. To investigate and detect objects in satellite images, ten different object classes were determined using the U-Net model for semantic segmentation [10]. Studies have been conducted on satellite images with a feature pyramid network (FPNet), which uses convolutional neural networks by combining feature maps from different scales [11]. Experiments have been conducted using the SegNet application and SUIM dataset, comparing image quality-enhancement and data-augmentation methods [12]. These are the image-segmentation networks used initially, but nowadays, backbone modifications or ensemble methods are widely used due to their efficiency. In this regard, semantic segmentation was performed on breast cancer images with the DenseNet-121 model and the attention-based pyramid scene-parsing network called Att-PSPnet [13]. A new method has been proposed for segmenting underwater images using Unet architecture and DenseNet as an encoder for semantic segmentation tasks [14]. Another work introduces a new data-driven framework called DatUS [15]. This framework provides dense semantic segmentation for unlabeled image datasets using a self-supervised image transformer and trained on low-contrast images. A loss function based on the active contour method and level set methods is proposed, and the integration of these methods with CNN architectures such as U-Net and DeepLabV3+ are studied on underwater images such as SUIM, RockFish, and DeepFish [16]. In the study conducted for multi-class semantic segmentation of high-resolution aerial images [17], modified residual block and dense spherical spatial pyramid pooling module were combined with the U-Net scheme and a better result was achieved compared to SOTA methods. Adding two objects with Gaussian blurring gave the best results and the performance was improved by using synthetic data [18]. In another study, a visual serving method that is more efficient than SOTA approaches and provides a balance between performance and computational efficiency was proposed [19]. In the study where a dual-resolution representation was developed [20] using fine superpixels for rare classes and coarse superpixels for background areas, a balanced semantic segmentation result was achieved within the scope of CNN feature representation for both frequent and rare classes, thanks to the shape information obtained. The backbone network, which is used in semantic segmentation as well as image classification and segmentation tasks, represents the main structure of the general network. This backbone architecture is adopted by networks such as VGG-net, AlexNet, GoogLeNet, and ResNet, which significantly outperforms much deeper models on the various challenging tasks [21]. Another study proposes a UISS-Net network for underwater scenes by using the ResNet18, ResNet34, and ResNet50 as the backbone networks that use the Unet to improve the performance of feature extraction for the backbone network [22]. Vision Transformers (ViTs) are also successful in image classification; however, they face several challenges when directly applied to dense prediction tasks such as semantic segmentation and object detection [23]. To overcome challenges like low visibility and variable lighting conditions, where traditional methods fall short, a hybrid model combining Swin Transformer and ConvMixer mechanisms, called SwinConvMixerUNet, has been developed [24]. To improve the quality of semantic segmentation, another work introduces a new deep learning framework called Swin Transformer U-Net (DS-TransUNet), utilizing the hierarchical structure of Swin Transformer in both the encoder and decoder [25].