## 3 Results
### Cnn  
In the CNN model, the training process was initiated by first separating the test images from the training dataset to ensure an unbiased evaluation. The remaining images were then divided into nine equal parts for training and validation using the k-fold cross-validation approach. In this method, eight parts, representing 80% of the dataset, were used for training in each iteration, while the remaining one part, accounting for 10% of the dataset, was designated for validation. The final 10% of images were set aside for independent testing. This structured division was strictly followed to prevent data leakage, ensuring that the test images were not used during training and preserving the integrity of the evaluation process. The CNN architecture under consideration consists of 21 layers in total. The sequence of layers is as follows: firstly, an ImageInputLayer is used as the input layer, followed by five Convolution2 dLayers, five ReLUlayers, and five MaxPooling2 dLayers. Subsequently, two fully connected layers, one ReLU layer, one softmax layer, and one classification layer are employed. The function of the latter is to facilitate classification. Each convolution layer comprises 128 filters, with the filter size set at \(5\times 5\). The rectified linear unit (ReLU) was employed as the activation function following each convolution layer. The pooling process was performed as maximum pooling (max pooling) with a size of \(2\times 2\) after each convolution and ReLU block. The fully connected layers (fullyConnectedLayer) comprise 256 and 3 neurons, respectively. The softmax layer obtains the probabilities in the output layer, and the final layer performs classification. In addition, the internal functions of MATLAB (namely, train network and classify) were utilized during the training and evaluation phases, with the models being trained on a GPU. Stochastic gradient descent (SGDM) optimization and cross-validation (k-fold cross-validation, K = 9) were preferred in the training process. The total number of layers was determined by considering all components in the deep learning model utilized. The model begins with an image input layer ([320 320 3]), which processes the input images. For feature extraction, the architecture includes five convolutional layers (Convolution2 dLayer), five ReLU activation layers (ReLUayer), and five max-pooling layers (MaxPooling2 dLayer). The fully connected and output layers  
Figure 18: YOLOv9-e model results. consist of one fully connected layer (256), one ReLU activation, and one fully connected layer (3), followed by a softmax and a classification layer. Summing these components, the model consists of 21 layers in total. From a neuron activation perspective, the fully connected layers play a decisive role. The fullyConnectedLayer (256) consists of 256 neurons, while the fullyConnectedLayer (3) has three neurons. Although ReLU layers are activated after convolutional and fully connected layers, they do not directly contribute to the total neuron count. Based on this structure, the model's total number of active neurons is calculated as 259. In terms of architectural design, the CNN model was configured with the following parameters:  
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* Padding: 1;
* Stride: 2;
* Filter Size: 5 \(\times\) 5;
* Number of Filters: 128;
* Total Layers: 5. Each layer was designed with optimized stride, padding, and activation functions to ensure effective feature extraction and classification. The training parameters were configured as follows:  
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* Optimizer: stochastic gradient descent with momentum (SGDM);
* Initial Learning Rate: 0.001;
* Maximum Epochs: 70;
* Execution Environment: GPU;
* Mini-Batch Size: 32;
* Momentum: 0.9;
* L2 Regularization: 0.0005;
* Gradient Threshold: 1;
* Validation Frequency: 30;
* Verbose: False.