## 2 System Configuration and Methods of Analysis  
The innovative method of this research is to combine UAV (Table 2), a dual lens camera (FLIR Duo R) which is equipped with visible light and IR lens, with real-time analysis software for solar module detection. Figure 1 shows the UAV and camera con  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{3}{c}{**Method**} \\ \cline{2-4}  & **UAV** & **IR Image Defect** & **Other Defect** \\  & & **Detection** & **Detection** \\ \hline [PERSON], et al. [2] & yes & yes & \\ [PERSON], et al. [4] & & yes & bypass diode \\ [PERSON], et al. [8] & yes & yes & \\ [PERSON], et al. [9] & yes & & digital map \\ [PERSON], et al. [10] & & yes & bypass diode \\ [PERSON], et al. [11] & & yes & \\ [PERSON], et al. [12] & yes & yes & \\ [PERSON], et al. [13] & yes & yes & deep learning \\ [PERSON], et al. [14] & yes & yes & diagnosis \\ [PERSON], et al. [15] & & yes & \\ [PERSON], et al. [16] & yes & yes & \\ [PERSON], et al. [17] & & yes & \\ [PERSON], et al. [18] & & yes & \\ [PERSON], et al. [19] & & & EL 1 \\ [PERSON], et al. [20] & yes & yes & \\ [PERSON], et al. [21] & yes & yes & \\ \hline \hline \end{tabular}  
* \({}^{1}\) Electroluminescence Technique.  
\end{table}
Table 1: Comparison of related references on solar panel monitoring.  
taining infrared and visible light adopted in this study. The schematic diagram of the proposed solar modules monitoring/UAV system is shown in Figure 2. This real-time analysis system requires a ground station for control and analysis. The ground station includes a control receiver and a computer equipped with MATLAB software. When the drone flies over solar power modules and takes photos with dual lens above, images are instantly transmitted to the ground station through the integrated radio frequency (RF) channel for analysis. The ground station can record modules' images and exam the health conditions of the modules in real time. Recorded images are analyzed in a computer equipped with MATLAB software. The output results may provide maintenance personnel a good reference for determining whether replacement of the module is necessary for improving the performance and extending the life of the solar module. In addition, fixing early-detected defects in time may prevent the deterioration of the module and reduce maintenance costs [8,22]. However, it is difficult to interpret the acquired IR image due to uncertain factors such as image quality, weather conditions, shadows during inspection and reflection of sunlight. Therefore, it would be beneficial using software tools to facilitate the defect and fault detection process [23]. On the other hand, IR images can help to detect hot spots in the solar module while the visual light images can differentiate the spots caused by light reflection and abnormal high temperature. These hot spots provide indications of abnormal operations that may result in a reduction of power generation or damage to the solar system. The main steps for the proposed system are shown in Figure 3.  
The main purpose of implementing image analysis technology is to detect defects in solar modules. This process is divided into two stages. The first stage is to find out the status and the location of defects in a solar module with Gaussian filter image analysis. This method has been confirmed in a previously published paper to identify defects [8]. The second stage is an innovative method for evaluating the extent of these verified defects.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Type & Six-axis drone LION-690 \\ Propeller & 13 inch \(\times\) 6 \\ Weight & 6 kg \\ Load & 2 kg \\ Mission Altitude & 1–10 m \\ Cruise Speed & 5–10 m/s \\ Sensor Resolution & 160 \(\times\) 120 \\ \hline \end{tabular}
\end{table}
Table 2: Unmanned aerial vehicle (UAV) specification and parameters.  
Figure 1: UAV system with thermal and visible light cameras: (**a**) UAV, (**b**) FLIR Thermal Camera.