# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 1 Introduction  
According to REN21, over the past two years, global photovoltaic (PV) plants capacities and annual additions have grown and expanded rapidly. For instance, 621 GW were installed in the year 2019 and 760 GW in the year 2020 [1], despite the reduction in electricity consumption and shifted daily demand patterns due to the COVID-19 pandemic [2]. Additionally, it has become one of the most profitable options and is an energy resource that has recently decreased in cost. As a result, solar electricity generation has grown in residential, commercial, and utility-scale projects [3]. The future of PV generation will focus on optimizing hybrid systems [4] and improving the performance of each element of the system, as well as reducing their cost due to large-scale production [5]. Furthermore, the sector trend has been asking for low prices, and the competitive market has encouraged investment in solar PV technologies across the entire value chain, particularly in solar cells and modules, to improve efficiencies and reduce the levelized cost of energy (LCOE) [1]. As a result, PV power plants could grow almost sixfold over the next ten years, reaching acumulative capacity of 2840 GW globally by 2030 and rising to 8519 GW by 2050, according to [6]. Thus, it is established that the number of PV plants and generated power have increased around the world. This implies specific technical challenges in their maintenance and operation (O and M) [7]. Some of these threats affect their production, which increases cost, and decreases profitability. The most common threats are the failures in the inverters and PV modules [8]. Through dirty equipment, the state of the environment, or manufacturing defects in the PV modules, PV plant energy generation can be curtailed by 31% in the worst cases [9; 10; 11]. One must consider that PV plants are commonly installed on roofs, rooftops, canopies, or facades for urban environments. Likewise, solar farms utilize rural environments, such as deserts, plains, and hills [7; 12; 13]. Depending on the location of the PV plant, the manual inspection tasks could be exhausting and take up to 8 h/MW, if the number of modules is considerable. The amount of inspection time increases for solar PV plants on rooftops or canopies, by virtue of aspects of their installation [14]. In addition, inspections to detect threats in the panels must be conducted by trained personnel. In some cases, problems occur in elevated installations, for which special training and certification is required. These jobs could put people and facilities at risk [15]. In recent decades, unmanned aerial vehicles (UAVs) have been increasingly used in inspection and patrol tasks [16; 17; 18]. UAV-based applications for PV plant inspections have many advantages in comparison with the manual inspection methods. The main advantages are flexibility, lower cost, larger area coverage, faster detection, higher precision, and the capacity to perform a superior and automatic inspections [11; 17; 18; 19; 20; 21]. There are many approaches to performing an inspection with UAVs in PV modules. One of them used UAVs with a thermal imaging camera to take photos in the infrared spectrum to evaluate the UAVs parameters, such as height, speed, viewing angle, sun reflection, irradiance and temperature, all of which are necessary to perform defect inspection [22]. In a second approach, UAVs were implemented to inspect different solar PV plants, wherein analysis of the correlation between altitude and the pixel resolution was used to detect PV panel defects and features like shape, size, location, and color, among others, of a particular defect were also detected [7; 23; 24; 25; 26]. In a third approach, the authors proposed PV plant fault inspection with UAVs using image mosaics combined with orthophotography techniques to create a digital map; image mosaics were combined with multiple visible and/or infrared range images into a single mosaic image covering a large area [14; 27; 28].