The reduced energy output of photovoltaic systems can often be traced back to a decrease in the efficiency of photovoltaic modules due to various abnormal operating conditions, such as faults or malfunctions. Common issues affecting performance include: accumulation of dirt, hotspots, electrical faults, cracks, breakages, burns, and surface damage. The severity of the impact depends on numerous parameters and conditions. As the global demand for solar energy grows, so does the importance of automated defect detection in solar panels. Deep convolutional neural networks (CNNs) have demonstrated significant potential in addressing image classification tasks across a range of domains. In this study, we implement a CNN model to assess the surfaces of photovoltaic panels and identify defects. Initial results indicate that the model achieves an accuracy of 65% when applied to infrared thermal imagery and up to 85% for images captured under visible light conditions.