## 5 Methodology
### Feature Extraction  
#### 5.1.1 Image Features  
In this study, CNN architectures were employed for feature extraction from images. CNNs are known for their ability to automatically learn both low-level (e.g., edges, textures) and high-level (e.g., shapes, objects) representations from images. To this end, features were extracted from fish images using four CNN-based deep learning models with varying sizes and complexities: ResNet50, MobileNetV2, EfficientNet-B2, and ConvNeXt. These models were deliberately chosen to reflect a broad range of architectural characteristics and computational requirements. ResNet50 is preferred for its high accuracy and deep residual learning capabilities. MobileNetV2 was selected for its lightweight design, which makes it suitable for resource-constrained environments. EfficientNet-B2 offers a well-balanced trade-off between performance and computational efficiency through compound scaling. ConvNeXt, on the other hand, is a modern CNN architecture inspired by Vision Transformers, while preserving the strengths of convolutional designs. A transfer learning approach was adopted using pretrained ImageNet weights, without applying any additional augmentation or normalization steps during feature extraction [24, 25]. During feature extraction, the convolutional layers of all pretrained models were frozen, and no fine-tuning was applied. The input images were resized according to the input size requirements of each model: 224 \(\times\) 224 pixels for ResNet50, MobileNetV2, and ConvNeXtBase and 260 \(\times\) 260 pixels for EfficientNet-B2. Brief descriptions of the models used are given below. * ResNet50: ResNet is a CNN architecture based on blocks called \"residual blocks\", developed to solve learning problems in multilayer deep networks [26]. ResNet models are named according to the total number of layers that they contain. Versions with different sizes, such as ResNet34, ResNet50, ResNet101, and ResNet152, can be selected depending on the size of the dataset and the processing resources. Although ResNet is generally superior in terms of accuracy performance, its disadvantages include long training times and high memory requirements. In this study, the medium-sized ResNet50 model was used. * MobileNet: MobileNet is a CNN model developed for use in studies with low system requirements [27]. MobileNet is based on the \"depthwise separable convolution\" approach, developed to significantly reduce the computational cost used in classical convolution and to create lightweight network models. With this approach, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Images** & **Number of Laser Samples** \\ \hline Day 1 & 258 & 258 \\ Day 2 & 256 & 256 \\ Day 3 & 256 & 256 \\ Total & 770 & 770 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of matched image and laser samples used in the dataset over three days. number of parameters and the computational cost of the model are significantly reduced. MobileNet has different versions, such as v1, v2, and v3. The MobileNetV2 version was used in this study. * EfficientNet: EfficientNet, which has versions of different sizes between B0 and B7, is a CNN model developed to save resources with fewer parameters [28]. EfficientNet has both small- and large-scale versions: smaller models, such as EfficientNet-B0, offer low resource requirements, while large models, such as EfficientNet-B7, provide high accuracy. EfficientNet can provide similar accuracy with fewer parameters compared to models such as ResNet, making it an attractive option in terms of resource savings. The medium-sized EfficientNet-B2 model was used in this study. * ConvNeXt: ConvNeXt is an up-to-date CNN model designed for modern computer vision tasks and was inspired by the success of Visual Transformer (ViT) architectures [29]. ConvNeXt was developed to take the simple and modular structure of ViT and combine it with CNN-specific features.