Infrared fault images have the limitations of a low recognition accuracy and low detection rate in a PV module inspection task using a UAV. To address these issues, a feature enhanced YOLO v5s fault de-tection algorithm is proposed. First, the loss function is optimized, the original regression loss calculation method is changed from GIOU to EIOU, and the confidence loss balance coefficient is adjusted adaptively to improve the model training. The InRe feature enhancement module is then added before each detection layer to enhance the ability of the target feature extraction by enriching the feature expression. Finally, compara-tive experiments are conducted using the infrared photovoltaic dataset created in this study. The experi-mental results show that the detection mAP of our method is 92.76%, whereas the detection speed is 42.37 FPS. The mean average precisions of the hot spot and component falling off were 94.85% and 90.67%, re-spectively, which can fully meet the requirements of the automatic inspection of the UAV.