The defects in photovoltaic cells affect the service life and power generation efficiency of the entire photovoltaic system. Aiming at the high missed detection rate of weak and small defects in the automatic detection of existing cells, a feature-enhanced lightweight convolutional neural network model was established. The feature enhancement extraction module was designed specifically to improve the extraction ability of weak boundaries. In addition, according to the principle of multi-scale recognition, a small target prediction layer was added to realize multi-scale feature prediction. In the experimental test, the mean average precision (mAP) of the model reaches to 87.55%, which is 6.78 percentage points higher than the traditional model. Moreover, the detection speed reaches to 40 fps, which meets the accuracy and real-time detection requirements.