# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 2 Related Works  
This section discusses about previous drone-based infrared thermography methods for PV module fault detection and the limitations of those works. Researchers across the world have studied infrared thermography for more accurate solar panel fault detection. [PERSON] et al. [12] proposed a novel monitoring system using UAV that detects several photovoltaic modules' failures. Thermal imaging and visual cameras were used as monitoring tools for inspecting PV power station. [PERSON] et al. [13]designed an algorithm which detects the defects and failures on PV systems using thermography analysis. Their algorithm detected the hot temperature area in infrared images.  
First, the original image was transformed to gray scale, which was followed by Gaussian filtering to reduce noise. Then, binary model was applied on the resulting images to distinguish hot and cold regions in the PV modules. Finally, the use of Laplacian model presented the characteristics of defective parts and determined the boundary area of the panel. A similar approach was proposed by [PERSON] et al. [15], who computed the percentage of degradation and boundary area. [PERSON] et al. [14] captured images using a drone at varying altitude to investigate the relationship between the altitude of image capture and possible defect identification in PV modules. However, these methods [12; 13; 14] can only detect the defects and not the exact location of the fouled PV module. This limitation hinders fast repairing process after detection of defective modules. [PERSON] et al. [16] proposed image processing pipeline for the automatic detection of individual photovoltaic modules, analysis of malfunctioned modules using infrared thermography, and classification into three main groups: overheated modules, hot spots, and overheated substrings. [PERSON] et al. [17] proposed a solar panel fault detection system using drones, thermal images, and RGB images. They detected the position of the solar panel array using RGB image and used thermal images to detect the faulty panels. Although these methods [15; 16; 17] can detect and localize the defective modules, they have been tested on a small scale dataset or in an artificial environment. On the contrary, some studies [9; 10; 11] have focused on the detection of fouling in large-sale PV panel systems. In [9], a computer vision approach was applied to identify and detect anomalies in PV modules in large-scale PV plants. The authors integrated geographic information with the results of template matching algorithm applied to thermal images, allowing panel identification by assigning identifier to each module during different flight sessions. In [10], a drone-mounted infrared thermography system was designed to rapidly detect fouling on PV panel systems. K neighbor mean filter was used for preprocessing the infrared images and single PV module in each image was recognized and extracted. In [11], the internal and external faults were detected by using thermal and charge-coupled device (CCD) cameras mounted on a drone. Morphological transformations and canny edge detection were used to find hotspots in the modules and the seed selection algorithm was used to determine the defective and the background pixels. These methods can detect and localize faulty modules and have been tested on real large-scale PV power stations. However, the drones used in these methods are remotely controlled by a technician, which adds manual labor to the procedure.