## 1 Introduction  
The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. For example, an 18 MWp plant with the availability of 99% (1% caused by uncontrolled failures) can cause economic losses up to 156K euros per year [1]. The malfunctions of a PV system are typically detected only if a significant reduction in energy production has been observed, while the time until the damage is determined can last up to a year [2]. Thus, a reduction of up to 18.9% of the annual energy production can be caused due to undetected faults in a PV system [3], which significantly affects the financial efficiency of the PV installation. The energy production of PV modules is degraded by faults, such as hot spots, cracks, or deposition of dirt, among others [4]. Additionally, the Potential Induced Degradation of solar cells may also reduce their energy production by up to 30% after a few years of operation. Overall, the faults of PV modulescomprise up to 73.5% a PV plant malfunctions [5]. Thus, early and reliable detection of faults is a key aspect for the monitoring of PV plants and their effective operation during their lifetime period. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. At present, PV plant monitoring is carried out using one of the following approaches: (a) electrical performance measurements and (b) image processing techniques. The first approach presents limited fault detection capabilities, it is both costly and time-consuming. Additionally, it is incapable of fast identification of the physical location of the fault. In the same way, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures. IRT imaging provides a real-time two-dimensional image of PV module from which temperature distribution of the module surface can be assessed [6; 7; 8]. Hence, IRT imaging is a valuable non-destructive inspection technology to identify, classify, and locate PV modules operating with defects. The identification and classification are based on temperature anomalies present on the surface of PV module. However, the setup and processing are rather complex for manual IRT imaging analysis and an experienced human operator is required. Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules have been identified as a cost-effective approach that offers 10-15 fold lower inspection times than conventional techniques [9]. However, the implementation of a cost-effective method to scan and check huge PV plants represents major challenges, such as the cost and time of detecting PV module defects with their classification and exact localization within the solar plant [10]. Moreover, many technological considerations should be taken into account for a proper inspection of photovoltaic plants [11]. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects. The main contribution of this work is the proposal of a comprehensive procedure for automated thermal image analysis. This procedure addresses from image pre-processing (e.g., image undistortion), the detection and classification of the defects, to the final location of the thermal defects. Moreover, this procedure also considers the best practices and conditions for image acquisition. Finally, the contribution also addresses several procedures to make the processing procedure as robust as possible (e.g., the detection of the panels surface in order to filter the thermal defects, or a quality indicator that checks if the individual panels detection was correctly performed, among others).