Resumen
The Ground Penetrating Radar (GPR) method is a commonly used method for earth dam disease detection. However, the major challenge is that the obtained GPR image data of earth dam disease mainly relies on human judgment, especially in long-distance earth dam disease detection, which may lead to misjudgments and omissions. The You Only Look Once v5s (YOLOv5s) algorithm is innovatively employed for GPR image recognition to address the current challenge. The YOLOv5s neural network model has advantages over the traditional convolutional neural network in terms of object detection speed and accuracy. In this study, an earth dam disease detection model was established based on YOLOv5s. Raw images from actual earth dam disease detection and GPR forward simulation images were used as the initial dataset. Data augmentation techniques were applied to expand the original dataset. The LabelImg annotation tool was employed to classify and label earth dam disease, thereby creating an object detection dataset that includes earth dam disease features. The model was trained within this dataset. The results indicate that the total loss function of the model trained on the custom dataset initially decreases and then stabilizes, showing no signs of overfitting and demonstrating good generalizability. The earth dam disease detection model based on YOLOv5s achieved average precision rates of 96.0%, 95.5%, and 93.9% for voids, seepage, and loosening disease, respectively. It can be concluded that the earth dam disease detection model based on YOLOv5s may be an effective tool for intelligent GPR image recognition in identifying earth dam disease.