# A Cyber-Physical System for Wildfire Detection and Firefighting
## 2 Related Work
Additionally, the use of unmanned systems offers many advantages, including the ability to cover large areas in any weather condition, operate during both day and night with extended flight duration, be quickly recovered and relatively cost-effective compared to other methods, carry various payloads for different missions within a single flight, and efficiently cover larger and specific target areas [25]. UAVs equipped with computer vision-based remote sensing systems are becoming a more practical option for forest fire surveillance and detection due to their low cost, safety, mobility, and rapid response characteristics [26]. Unmanned Aerial Vehicles (UAVs) based on computer vision fire detection can provide immediate information on the fire spots but are also equipped with a dropping mechanism used to target and shoot the fire by releasing extinguishing balls [13; 27]. Popular real-time object detection systems, such as YOLO can be integrated into UAVs, providing real-time monitoring of large forest areas and detecting fires at an early stage, reducing the risk of the fire spreading [28]. Consequently, numerous research studies have been conducted in recent years to develop UAV-based forest fire monitoring and detection applications based on YOLO [12; 29; 30]. In addition, autonomous ground systems such as the UGV are essential in responding to wildfire emergencies. Equipped with advanced technologies such as computer vision, infrared and depth cameras, and other sensors, UGVs can be used for detecting and extinguishing fires [31; 32; 33], as well as cooperating with other systems such as UAVs [14]. These robots can perform tasks on land without human intervention and access difficult-to-reach areas that may be too hazardous for human firefighters. Forest fire monitoring and detection applications using Unmanned Aerial Vehicles (UAVs) have received significant attention in recent years, with a particular focus on employing the You Only Look Once (YOLO) algorithm for object detection [12; 29; 30]. These studies have demonstrated the effectiveness of UAVs in providing aerial surveillance and timely fire detection capabilities. Furthermore, autonomous ground systems such as Unmanned Ground Vehicles (UGVs) have emerged as crucial elements in responding to wildfire emergencies, leveraging advanced technologies such as computer vision, infrared and depth cameras, and other sensors [31; 32; 33]. UGVs are capable of detecting and extinguishing fires and collaborating with UAVs to access hazardous or hard-to-reach areas that pose risks to human firefighters [14]. Object detection algorithms play a critical role in fire detection systems as they enable real-time identification and localization of fire incidents. The You Only Look Once (YOLO) family of object detection algorithms has proven highly effective and efficient in computer vision applications. Notably, YOLOv4 (https:  
Figure 1: Architecture of forest fire detection. //blog.roboflow.com/a-thorough-breakdown-of-yolov4/, accessed on 19 April 2023) and YOLOv5 ([[https://blog.roboflow.com/yolov5-improvements-and-evaluation/](https://blog.roboflow.com/yolov5-improvements-and-evaluation/)]([https://blog.roboflow.com/yolov5-improvements-and-evaluation/](https://blog.roboflow.com/yolov5-improvements-and-evaluation/)), accessed on 19 April 2023) stand out as prominent versions, each offering distinct advantages for different components as shown in Table 1. In our study, we integrated YOLOv4 into the UGV component, leveraging its state-of-the-art accuracy and compatibility with the darknet framework and Robot Operating System (ROS). This integration facilitated object detection and localization within the UGV, enabling seamless collaboration within the overall system architecture. To extend the capabilities of our system, we incorporated YOLOv5 into the UAV component. YOLOv5 features a streamlined architecture and optimized data augmentation strategies, making it ideal for resource-constrained UAVs.