## 1 Introduction  
Many semantic segmentation studies in the computer vision area have been conducted with datasets taken from multi-class and different feature-imaging systems [1; 2]. Thanks to deep learning models, very successful results can be obtained in segmentation and detection studies on large image datasets even if the class is unbalanced. Unfortunately, performance-improvement studies on underwater datasets are still needed [3]. The aim is to increase the visual quality of underwater images that must cope with low-contrast and visibility problems by combining global and local contrast-enhancement techniques [4]. Due to the difference in pressure between water and air, detecting moving objects underwater is a problem that needs to be dealt with. In addition, decreasing visibility and changing illumination levels can also make the situation difficult. A dataset presented in this regard, Semantic Segmentation of Underwater Images (SUIM), is the largest underwater dataset labeled for semantic segmentation [5]. Class imbalance, a problem that needs to be dealt with in performance evaluations, is also encountered in this dataset. In the computer vision research domain, semantic segmentation tasks can predict the pixel-wise masks and class categories of objects of interest based on RGB or hexadecimal coded pixels. Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-focused methods which show a salient performance. There are now methods in which modules are added to the Mask RCNN algorithm, that is one of the first deep learning-based semantic segmentation methods, and the perceptual fields of each layer are expanded and target classification is improved [6]. Trending studies today focus on determining more precise boundary regions. Various deep learning architectures have addressed semantic segmentation studies in numerous domains. [PERSON]. [7] proposed a new method for tackling multi-class semantic segmentation. Better results have been obtained than the state-of-the-art methodsby fine-tuning with annotations to improve model performance by addressing the problem of background ambiguities. In multi-class semantic segmentation, deep symmetric convolutional neural networks dominate the algorithmic state-of-the-art. The effect on the performance of semantic segmentation with U-Net has been detected using satellite images [8]. Semantic segmentation was performed in another study to predict damage classes using Convolutional Neural Networks (CNN) on a multi-class and class-imbalanced structured dataset [9]. A new framework that leverages CoordConv and Group Normalization based on Mask R-CNN to improve generalization for underwater marine animals has been proposed [3]. In light of these studies addressed by deep learning architectures, an encoder-decoder incorporated working flow was carried out thanks to the generalization ability of the architectures without considering any image-enhancement preprocessing. The key points of this study are as follows:  
1. Working on images taken underwater brings the problem of a low-contrast image quality. The images in the SUIM dataset have different sizes of resolution. Considering the computational cost and to obtain a standard suitable for all networks, all the RGB images and annotations were cropped as 256 \(\times\) 256 \(\times\) 3 patches. Afterwards, patches that do not contain any class information were detected and eliminated from both the annotation semantic context and the corresponding image path list. The process of cropping images into pieces helps to see and understand the broad connections between objects in the image. Moreover, focusing only on class objects without any loss of pixels is still sufficient for whole-scene segmentation, especially for such a low-contrast image dataset. 2. As a workflow strategy to evaluate training schemes, different variations of encoder-decoder dense frame fusions were used and the generalization ability of the models was increased.