# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 5 Discussion  
In this study, a system that can continuously capture polar images and detect fires was developed by combining a fisheye camera and a Raspberry Pi. A technique was developed to calculate the azimuth and elevation angles by detecting the point at which the fire occurred in the polar image, and to estimate the location on the map by using it together with the DSM, which represents the topography. The technology developed in this study will be useful for the early suppression of fires by detecting and locating fires at an early stage. However, there are still issues that need to be improved, which are summarized as follows. The fisheye lens used in this study can directly obtain polar images, and fire detection and azimuth and elevation angle calculations were applied to these polar images. However, as the pixels of the polar image are closer to the horizon, objects can be compressed. Therefore, to accurately detect fire in such polar images, it is recommended to use high-resolution images as much as possible, which means that an expensive lens is used and the size of the image data to be transmitted increases. In particular, when detecting a fire at a  
Figure 10: Image of the point where the GPS of the subject was recorded on DSM(Digital Elevation Model). (**a**) daytime experiment, (**b**) nighttime experiment. distant point, because the fire appears to be very small, it may be difficult to detect and to determine its exact location. In this study, the Wi-Fi function built into the Raspberry Pi was used to transmit images by using the hotspot function of the smartphone. However, it may be difficult to maintain an online status in a mountainous area where forest fires are very likely to occur. Therefore, for commercialization, it is necessary to think more deeply about online access. These considerations are equally needed for power supply. It is also necessary to think about whether it is better to reduce costs by using batteries or by supplying constant power through wires, even though this is somewhat costly. In this study, DSM made from drone images was used for location estimation. In the study area, there are not many trees, so it was possible to create a relatively sophisticated DSM. However, it will be difficult to make a sophisticated DSM because trees are dense in mountainous areas where actual field application is required. Therefore, it would be realistic to use an open DEM or DSM with slightly lower resolution for general use. Since the use of a DSM with low resolution means that errors in location prediction increase, it is necessary to make an optimal choice regarding whether to estimate a relatively accurate location by creating a high-quality DSM or to make a rough estimation at a lower cost. In this study, a forest fire was detected and localized with one fisheye lens, but a more accurate localization would be possible if multiple fisheye lenses were installed and each image was taken for the same fire. This method is similar to the principle of GPS, which uses three or more satellites to estimate a position. However, one of the advantages of this study is that it covers all directions with one device, so installing multiple devices in a small area is contrary to this advantage. Therefore, the optimal installation method should take into account the relationship between cost and accuracy, as mentioned above. In addition, the developed system observes the area of the hemisphere by installing one fisheye lens vertically upward, so it is impossible to observe areas that are lower than the system. For effective application, it is sometimes necessary to observe lower places by installing the lens on the top or middle of a mountain, so two fisheye lenses should be installed in one system to monitor all directions in the form of a sphere. In this study, the central point was extracted from the fire area and the location of the central point was calculated, but an actual fire can spread over a wide area over time.