The invention relates to the technical field of water turbine runner blade defect detection, and particularly discloses a water turbine runner blade defect detection method based on a YoloV4-Lite network, and the method comprises the steps: S1, constructing a defect detection network based on the YoloV4-Lite network; S2, performing picture collection on different defect positions of the turbine runner blade to obtain thousand or more defect pictures; S3, preprocessing the defect picture acquired in the step S2 (processing by using LabelImg software according to a Pascal VOC 2012 format) to obtain a data set; and S4, training, testing and verifying the defect detection network by adopting the data set. According to the invention, the backbone extraction network CSPDarkNet53 network of YoloV4-Lite is replaced by the MobileNet network, and the MobileNet network is a real-time lightweight network, so that the network detection speed can be improved, and the network parameters can be greatly reduced. Experimental results show that the accuracy rate of the defect detection network can reach 97.48%, the network parameter quantity of the MobileNetV3 only needs 37.35 MB and is reduced by 206.94 MB compared with that of CSPDarkNet53, the FPS reaches 44.68, and the method has the advantages of being high in accuracy rate, low in memory storage and real-time.