Abstract：When sonar imaging is used to detect the apparent defects of underwater piles and piers, the defect characteristics from the sonar images are remarkably different from those of the optical images, so that manual identifications prone to errors are required to detect the location and type of the defects. In order to solve this problem, this paper presents a novel deep learning and intelligent detection of apparent defects on underwater foundations of bridges based on sonar imaging. Firstly, the MS1000 sonar was used for imaging the underwater foundations and experimental models, and a large number of sonar images were obtained, so that the defects characteristics of the underwater foundations were analyzed based on these sonar images. Secondly, based the VGG16 network model, a modified faster region-based convolutional neural network（Faster R-CNN)was built. Data augmentation was implemented by rotation at every 90°, vertical and horizontal flipping operations. An approximate joint optimization training method was adopted. Rectangular bounding boxes for detection were obtained to locate the defects on underwater foundations with the category labels and classification probabilities. Finally, 150 images not used to form the training sample set were selected for detection, validating the effectiveness of the proposed method. Furthermore, the evaluation indexes, including confusion matrix, precision ratio, recall ratio, accuracy and F1 score, were employed to study the performance of the detection method. The results show that the F1 scores of the holes defects, spalling, displacement, and no defects are 90.1%, 84.9%, 78.7%, and 94.6%, respectively. The overall accuracy of detection is 883% and the average F1 score is 87%. This indicates that the proposed method is feasible and effective in the identification and positioning of underwater apparent defects by the automatic processing of sonar images, and provides technical support for the intelligent detection of underwater apparent defects and safety assessment of bridge.
