# A Cyber-Physical System for Wildfire Detection and Firefighting
## 2 Related Work
This integration empowered the UAV to perform real-time fire detection and localization, enabling efficient aerial surveillance and coordination with the UGV. By harnessing the strengths of YOLOv4 and YOLOv5 in our collaborative UGV and UAV system, we achieved robust object detection capabilities, seamless integration within the system framework, and real-time fire detection and localization. Our study builds upon the advancements made by previous research studies utilizing YOLO in UAV-based fire detection applications while also showcasing the effective integration of UGVs in firefighting operations. Research Gap  
Recently, the Internet of Things (IoT) has gained significant popularity as a rapid and effective technique for detecting fires, particularly in the context of forest fire prevention. The IoT forest fire system is designed to facilitate continuous monitoring and detection of forest fires, operating 24/7. This system relies on the strategic deployment of sensors throughout the forest, enabling comprehensive coverage. To establish seamless commun \begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **YOLOv4** & **YOLOv5** \\ \hline \multirow{4}{*}{Architecture} & Utilizes a complex architecture with a CSPDarknet53 backbone, which includes multiple convolutional layers and skip connections. & Introduces a simplified architecture that focuses on efficiency and speed. Replaces the CSPDarknet53 backbone with a smaller and more streamlined CSPNet-based architecture. \\ \cline{2-3}  & Larger model size, requiring more computational resources. Provides slightly slower inference times compared to YOLOv3. & Smaller model size, achieving faster inference times without compromising much on accuracy. \\ \hline \multirow{4}{*}{Performance} & Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks. & Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks. & Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks. & Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks. \\ \hline \multirow{4}{*}{Training Approach} & Adopts a traditional single-stage object detection training approach, utilizing anchor boxes and predefined anchor scales. Employs the focal loss function to address class imbalance issues. & Introduces a new approach called “Generalized Anchors,” which adapts anchor sizes during training. Incorporates a focal loss function and implements a novel loss function called “CIoU loss” to improve localization accuracy. \\ \hline \multirow{4}{*}{Availability} & Open-source implementation widely used and supported by the computer vision community. Available as a publicly accessible codebase. & Open-source implementation is gaining popularity due to improved speed and efficiency. Codebase is publicly available, allowing researchers and developers to utilize and contribute to its development. \\ \cline{1-1} \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of YOLOv4 and YOLOv5. nication among these sensors, a low-power wide-area network protocol, such as LoRa, is commonly implemented. Such protocols possess the capability to cover expansive forest areas. Leveraging the data collected by these sensors, combined with advanced machine learning algorithms, early-stage fire detection becomes achievable [34; 35]. However, one limitation of IoT is its incapability to directly extinguish fires. In order to overcome this limitation, researchers are investigating the integration of IoT with the robotic ecosystem, which holds promise for forest fire response.