The integrity of the blade surface is an essential guarantee for the safe operation of wind turbines. It is crucial to research damage detection of blade surface cracks. Based on the improved YOLOv5 model, D-YOLO-v5 is proposed by improving the loss function, objective classification function, and activation function, extending the application of image processing, artificial intelligence, and pattern recognition to investigate intelligent recognition of cracks on the surface of blades. The blade cracks object detection test was completed by LabelImg image annotation, D-YOLO-v5 training parameters adjustment, test parameters selection, prediction model construction, model training, and model prediction steps. The recognition of cracks under different conditions, such as normal light, dark light, and external interference, is subsequently completed. The values indicated that the object detection model parameters of D-YOLO-v5 were neither over-fitted nor under-fitted. In this case study, the accuracy of the blade surface crack-detection model is 98.62%, the mean average precision value is 49.12%, the recall value is 92.20%, and the recognition speed of a single image is 0.01 s. The simulation concludes that the proposed method can meet the accuracy and real-time requirements of practical engineering.