## 1 Introduction  
In recent years, many countries are actively developing renewable energy-related industries, and the construction of solar power plants is regarded as an important part of it. Taipower (a government power agency in Taiwan), for example, has stated that 2019-2020 is a bumper year for large-scale development projects, and it is expected to achieve the target of 2.2 GW solar installations set by the Ministry of Economic Affairs. Meanwhile, Taipower is expecting to complete its annual production plan of 6.5 GW before the end of 2021 [1]. It is believed that solar energy will become one of the most important renewable energy sources in Taiwan. However, the maintenance and monitoring will be a challenge as many large scale solar farms are operated and built in remote area and some panels are even installed on top of high rise buildings. The panel temperature is an important factor to determine its efficiency. For example, when the temperature increases by \(1\)\({}^{\circ}\)C, the power generation efficiency of solar modules decreases by 0.5% [2, 3, 4]. Therefore, an accurate and efficient way of monitoring and identifying the failure of solar modules is essential [5]. In addition, rapid detection of solar module failures can extend the service life and maintain the performance of the solar system [6]. To improve the detection efficiency, this paper proposes a fast, time-saving and real-time detection system to identify the failure status of solar panels. In this study, a drone equipped with a set of high-definition cameras with infrared and visible light functions was adopted to inspect the health conditions of a solar farm. Advantages of using drones include large area coverage, high flexibility, light weight, and fast speed, and thus module defect detection can be performed in a relatively short time after the shooting status is sent back to the ground station [7]. Lastly, the image analysis with MATLAB is conducted to determine the status of these modules. Using UAV to monitor a wide area of solar panels has been investigated by a lot of researchers previously. [PERSON] et al. has developed a rapid inspection system with the help of drone to inspect the solar power plant at the Czech University of Life Sciences in Prague and the PV power plant in Ladna [8]. Their findings agreed with the situations that were found in real field inspection. Their work was conducted only inside the laboratory and on the roof of the building and no real data on real fields were collected and analyzed. The authors of that paper consider roofs to be a very convenient place to install small PV power plants that can be integrated into buildings. They perceived the usage of agricultural land for large PV power plants is far less appropriate. [PERSON] et al. also proposed a real time inspection and fault detection system for PV modules [2]. Their system contains a thermal and a CCD camera. Reports mentioned that defects have been identified accurately in the PV modules using the proposed real time system. The proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude. [PERSON] et al. proposed a UAV-Based Digital Mapping system [9]. It allows easy access and completion of the information present in the database. This information management system and PV plant digitalization process are able to analyze and index the IR in the database and obtained visual images in photovoltaic plant monitoring properly. Monitoring the status of solar modules has become an important issue recently and many kinds of research have been conducted to find a quick and reliable way for faults detection. A review of the recently published papers is given in Table 1 which compares the way these investigations were carried out. It seems quite clear that UAV is a very convenient tool for large scale monitoring that many researchers use UAV for collecting data.