# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 1 Introduction  
In Korea, 64.5% of the territory is composed of mountainous areas, and the west wind generated by high pressure in the spring passes through the Taebaek Mountains, decreasing moisture and causing dry weather in the east. Therefore, the Yeongdong region of Gangwon-do in the eastern part of Korea is exposed to the risk of forest fire every year. Large-scale forest fires cause serious disasters all over the world, not only in Korea. In June 2019, a large-scale forest fire in Australia (11 million hectares) took 6 months to extinguish, and a forest fire in the western United States in 2021 (1.91 million hectares) also took 3 months. In Vancouver and neighboring British Columbia, hot and dry weather in the summer of 2021 caused more than 1500 forest fire cases [1]. For the early suppression of forest fires, research to detect forest fires at an early stage by using various sensing technologies is being actively conducted worldwide [2]. Fire detection systems can be classified into two types: a method of physically detecting a fire using various sensors and a method of detecting a fire using an image. For example, smoke can be detected through an ion sensor, or flame can be detected using an infrared sensor [3]. There is also a study using ultraviolet flame detection sensors [4]. When the sensor detects a small flame, a push message can be sent to a smartphone through the Raspberry Pi and the GCM server. These sensors perform well in an indoor environment, but in a large outdoor space, it is difficult for smoke or heat to reach the sensor, so detection may be delayed or erroneous [5]. To solve this problem, there have been studies sharing theresults detected by sensors by establishing a wireless sensor network (WSN) [6; 7; 8; 9; 10]. This method can share the detected results and can cover a relatively wide area as the number of devices increases, but has a disadvantage in that cost also increases. Recently, various image processing methods have been widely used to detect a fire in a wide area at a relatively low cost. In general, many studies have been conducted to analyze the color and movement of flames through image-processing, combining RGB color analysis and YCbCr color analysis [11; 12; 13; 14; 15; 16; 17; 18]. Various techniques, such as heuristic algorithms or fuzzy logic, can be used together to increase detection efficiency. Recently, machine learning technologies have also been used to detect fires in images [19; 20; 21; 22; 23; 24]. Although fire detection technology using RGB images is developing, there may be limitations in detection accuracy, and there have been studies using a thermal imaging camera to compensate for this [25; 26; 27]. Fire detection ability was analyzed using a thermal imaging camera according to the distance between the fire source and the thermal imaging camera. When compared with optical cameras, it was found that thermal imaging cameras are capable of earlier fire detection than optical cameras. The technology for detecting fires in images is expected to continue to improve. However, existing methods of detecting fire in an image or video cannot detect a fire out of the field of view. In addition, it is difficult to specify the location of the fire. There are some cases [28] where analysis results were shared by combining image-based fire detection technology and WSN, but the focus was on fire detection itself, not on localization. There are very few cases that estimate the location of a fire that can occur in a wide area, such as a forest fire. There have been studies [29; 30] using drones or satellite images to locate fire points in a wide area. However, since it is unknown when a fire will occur, it is difficult to monitor a fire in real time with these methods. Each of these existing fire detection technologies has its own merits, and there are appropriate options depending on the situation. However, there are limitations to their application to fires that can occur over a wide area, such as forest fires.