# A Hybrid Retina Net Classifier for Thermal Imaging
## 4 Results  
Figure 3 depicts the programmatically designed structure of the proposed neural network, illustrating the architecture and the connections between the layers. The classification performed here is binary classification with two classes. Some datasets are used for testing, and some are used for training. Based on the iterations, the outputs are as shown. Figure 4 showcases the accuracy achieved by the proposed network during the training and testing phases. It provides insights into the network's performance in correctly classifying the FLIR dataset. X-axis represents the number of epochs and Y-axis represents the accuracy. Figure 5 presents the loss function and the number of iterations during the training process. It demonstrates the convergence of the network and the reduction in the loss function over time. X-axis represents the number of iterations and Y-axis represents the amount of loss. Figure 4: Accuracy for the proposed network. Figure 3: Proposed neural network structure programmatically. The decreasing trend of the loss function in Figure 5 signifies that the network is effectively optimizing its parameters to better fit the training data. It indicates that the network is improving its ability to make accurate predictions and reduce errors during the training process. Monitoring the loss function during training is crucial as it helps in assessing the network's progress and determining if further training iterations are needed. It also aids in identifying potential issues, such as overfitting or underfitting, and guiding adjustments to the network architecture or training strategies if necessary. Figure 6 shows examples of positive cases where the input image contains a detected weapon. It visualizes the input image, the detected weapon, and the corresponding highlighted area. Figure 7 shows examples of negative cases where the input image contains a detected weapon. It visualizes the input image, the detected weapon, and the corresponding highlighted area. Figure 5: Accuracy of the network with loss and iterations. Figure 6: Weapon region detection from the input frame positive case:(**a**) represents input image; (**b**) represents detected weapon, i.e., positive case; (**c**) represents detected area. Figure 8 highlights the performance metrics of proposed HDNN. (Hybrid Neural Network), for a specific dataset. The metrics evaluated include total frames, true positive (TP), false positive (FP), false negative (FN), sensitivity, positive predictive value (PPV), false alarm rate, and accuracy. Table 2 represents the comparison of the proposed hybrid neural network (HDNN) with existing models. The proposed HDNN is compared with the DAG model, Haar transform + LBP, Kalman + Haar + LMP, Gaussian mixture models (GMM), and the existing convolutional neural network (CNN). Compared with the existing models, our proposed HDNN outweighed all of them in terms of accuracy. The proposed HDNN is implemented with 512 frames, which achieved accuracy of 0.99. In the existing methods, such as GMM, Kalman Filter (HaaR + LBP), Kalman + HaaR + LBP, detection using aerial thermal views, detection and tracking in thermal videos using direct acyclic graph, accuracy is low. In parameters, such as TP, FP, FN, sensitivity, PTV, false alarm rate, the proposed HDNN gives better results. The percentage of accurately classified instances is referred to as accuracy. The proportion of true positives that are successfully identified is measured by sensitivity or recall. The F-measure is a test accuracy metric. Precision, also known as positive predictive value,  
Figure 8: Performance analysis of the proposed method. Figure 7: Weapon region detection from the input frame negative case:(**a**) represents input image; (**b**) represents not detected weapon, i.e., negative case; (**c**) represents non detected area. is a measure of the number of relevant instances found among the retrieved instances. MCC assesses the accuracy of binary classification.