# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 4 Results  
The forest fire detection system developed in this study was applied to Mt. Bukjeong near the Samcheok Campus of Kangwon National University. The size of the target area is about 194,000 m\({}^{2}\), and the device was installed in a location (129.1660\({}^{\circ}\) E, 37.4518\({}^{\circ}\) N) with a wide view as a result of GIS analysis. Figure 6 shows the result of analyzing the visible area using viewshed analysis in QGIS to select a suitable area to install the system. Viewhed analysis is an algorithm that can determine the visible area from a given location by considering the elevation of each point from DSM data. Figure 6a is the result of analyzing the visibility index using DSM in the study area. A point with a higher visibility index value means a point where a wider area is visible or is visible from many points. The place marked with a red circle is the point where the system is installed, and it is a flat point with a high visibility index and good accessibility. Figure 6b shows the result of the viewshed analysis at the installation point, and the shaded areas are the points predicted to be visible from that point. On the other hand, in the case of a red triangle point with a low visibility index, only narrow areas are visible at that point, as shown in Figure 6c. Figure 7a shows a picture of the developed system installed in the study area. The system was installed after leveling it using a tripod and checking the north direction. Since it is important to know the height of the observation system to estimate the location of the forest fire, the height of the tripod was also measured. Figure 7b shows an example of an image taken through thesystem. Figure 7c,d present images taken through the camera of a Samsung Galaxy Note 10 with a horizontal angle of view of 61.5\({}^{\circ}\) and a vertical angle of view of 44.4\({}^{\circ}\). Figure 7c is the case where the direction of the camera is facing the front, and Figure 7d is the case facing the vertical top. Because the angle of view of a normal camera is narrow, it can be confirmed that a narrower area is captured than when using a fisheye camera. Figure 6: Estimation of a suitable location to install the developed system. (**a**) The visibility index result, where the red circle is the point where the value is high and the triangle is the point where the value is low, (**b**) the viewshed analysis result at the point where the visibility index is high, and (**c**) the result at the point where the visibility index is low. Figure 8a is the result of some of the continuous images taken during the day. The location of the moving person was marked with a red point, and it was detected after assuming that the red point was a fire. Aside from being unable to experiment with a real fire for safety reasons, it was useful to conduct the test this way to verify the accuracy of the position estimation. Although the experiment was conducted by detecting a moving person, the same method can be applied to an actual fire. The azimuth and elevation angles from the center point of each image to the red point (where the person is located) are shown in Table 1. For verification, the location of the moving person was recorded using GPS, and the GPS location at the time each image was taken is shown in Figure 9a as a yellow point. The location of the subject estimated through the polar image and DSM is shown in Figure 9a as a white point. The analysis points are about 25.0 m away from the observation point on average and, in this case, the average value of positional errors between the positions measured by GPS and the positions predicted from the polar image was about 0.97 m. Although there is a slight error between the location recorded by GPS and the estimated location, it was confirmed that a reasonable estimation is possible. Figure 8b is the result of some of the continuous images taken at night, and the subject is a person holding a flashlight.