# A Hybrid Retina Net Classifier for Thermal Imaging
## 5 Conclusions  
In this research, we utilized a hybrid retina net for the classification of images using thermal images from the FLIR dataset. The proposed model was trained and tested using these thermal images. Impressively, the model achieved an accuracy of 99.5% in classifying the objects within the thermal images. It is worth noting that processing thermal images computationally requires more time compared to regular JPEG images. However, by augmenting the network with additional layers, incorporating more training data, and leveraging the power of multiple GPUs, more accurate results can be obtained for object classification in thermal imaging. By stacking additional layers, the network can capture more intricate features and patterns present in the thermal images, leading to enhanced classification performance. Moreover, training the network with a larger and more diverse dataset can further improve the model's ability to generalize and accurately classify objects in thermal images. Furthermore, leveraging the computational capabilities of groups of GPUs can significantly expedite the processing time for these thermal images, allowing for faster inference and analysis. This can be particularly advantageous in real-time applications or scenarios where quick decision-making based on thermal imaging data is crucial. This research proposes a groundbreaking approach by utilizing a hybrid neural network for detection and classification tasks using thermal imaging. The proposed technique has demonstrated exceptional accuracy even under challenging conditions, such as extreme changes in illumination, occlusion, and longer distances. One notable advantage of the suggested system is that it eliminates the need for pre-processing steps, such as image rectification and enhancement in thermal imaging. These pre-processing steps are typically employed to improve the quality of the image before detection and classification tasks. However, the proposed approach leverages the optimal features inherent in the thermal images themselves, allowing for more accurate tracking of objects without the need for extensive pre-processing. Additionally, the research highlights that earlier feature selection methods can have a detrimental impact on the training of CNN designs. By adopting the hybrid neural network approach, the proposed \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Sensitivity**} & \multicolumn{2}{c}{**Positive**} & \multicolumn{1}{c}{**False**} & \multirow{2}{*}{**Accuracy**} \\  & & & & & & & & & **Value** & **Rate** & \\ \hline
1 & Proposed HDNN & 512 & 440 & 30 & 42 & 0.99 & 0.966 & 0.036 & 0.99 \\ \hline
2 &
\begin{tabular}{l} Object detection and tracking in thermal \\ video using DAG- Directed Acyclic Graph \\ \end{tabular} & 184 & 165 & 10 & 14 & 0.977 & 0.88 & 0.054 & 0.98 \\ \hline
3 &
\begin{tabular}{l} People detection and tracking from aerial \\ thermal views \\ \end{tabular} & 1282 & 950 & 124 & 208 & 0.97 & 0.85 & 0.044 & 0.95 \\ \hline
4 & Kalman Filter (Haar + LBP) & 128 & 102 & 16 & 10 & 0.95 & 0.89 & 0.032 & 0.92 \\ \hline
5 & Kalman + Haar + LBP & 128 & 110 & 10 & 8 & 0.95 & 0.87 & 0.032 & 0.93 \\ \hline
6 & GMM & 128 & 100 & 8 & 10 & 0.95 & 0.87 & 0.032 & 0.94 \\ \hline
7 & Existing CNN & 512 & 410 & 60 & 42 & 0.95 & 0.912 & 0.032 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Proposed and Existing Model. system overcomes this limitation and achieves superior performance in object detection and classification tasks. The ability to detect and classify objects accurately in thermal imaging without relying heavily on pre-processing or post-processing techniques is a significant advancement. It enables the system to effectively handle challenging scenarios where traditional methods may struggle.