Aiming at the problem of detecting defects and determining their relative distances in centralized photovoltaic panels inspected by unmanned aerial vehicles, a photovoltaic panel defect detection and location system based on deep learning YOLOv11n is proposed. Defect detection of high-resolution photovoltaic panel images is carried out by using YOLOv11n combined with slicing assisted hyperreasoning. Reference points and target points are set up in the centralized photovoltaic panel array based on the detection results. The detection result information is integrated with the information of unmanned aerial vehicle equipment and the exchangeable image file format information of the data set to calculate the longitude and latitude of the reference points and target points, thereby calculating the relative distance between them and achieving the defect location of photovoltaic panels. The experimental results show that the mAP50 and mAP50:95 of the YOLOv11n training results are 67.1% and 49.5% respectively; the accuracy rate of target detection combined with slicing assisted hyperreasoning is 88.73%. The accuracy rates of the defect location algorithm with errors ranging from 0 to 4 meters under visible light and thermal imaging cameras were 96.73% and 97.64% respectively, basically meeting the operation and maintenance requirements. Finally, an interactive management system page is constructed by combining front-end development languages such as JavaScript, and the detection information is stored in the database. The system realizes the intelligence of defect detection and location of photovoltaic panels through fault detection, location and information storage of photovoltaic panels, providing technical support for the maintenance of centralized photovoltaic panels.