The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting. An automatic intelligent detection and tracking system for pavement cracks is proposed. Our system is formed of a pavement crack generative adversarial network (PCGAN) and a crack detection and tracking network called YOLO-MF. First, PCGAN is used to generate realistic crack images, to address the problem of the small number of available images. Next, YOLO-MF is developed based on an improved YOLO v3 modified by an acceleration algorithm and median flow (MF) algorithm to count the number of cracks. In a counting loop, our improved YOLO v3 detects cracks and the MF algorithm tracks the cracks detected in a video. This improved algorithm achieves the best accuracy of 98.47% and F1 score of 0.958 among other algorithms, and the precision-recall curve was close to the top right. A tiny model was developed and an acceleration algorithm was applied, which improved the detection speed by factors of five and six, respectively. In on-site measurement, three cracks were detected and tracked, and the total count was correct. Finally, the system was embedded in an intelligent device consisting of a calculating module, an automated unmanned aerial vehicle, and other components.

This letter proposes a method based on deep learning for the automatic recognition and localization of underground pipelines using the ground penetrating radar (GPR). First, an automatic recognition model with an average precision (AP) of 0.9256 is proposed and trained based on Faster R-CNN. The feature extraction is optimized by the Attention-guided Context Feature Pyramid Network (ACFPN), and the cascade structure is used to improve the detection frame regression accuracy. Moreover, using Tesseract OCR, a positioning model is developed based on recognition results to obtain the burial and horizontal position of the pipeline. Furthermore, on-site experiments were carried out on real embedded pipes to verify the feasibility and effectiveness of the developed method. The absolute error of the localization data is lower than 11 cm, and the average error ratio is smaller than 12%. Consequently, it is demonstrated that the proposed method is considerably automatic, efficient, and reliable for the recognition and localization of underground pipelines.

Regular damage detection plays an important role in timely pavement maintenance. However, the existing detection methods struggle to efficiently and accurately identify the category and contour of the damage. Therefore, this paper proposes a Road-Mask R-CNN mobile damage detection model to automatically segment and measure multiple pavement damages. First, the optimized k-means clustering algorithm is used to intelligently determine the size and ratio of the anchor. Subsequently, the traditional nonmaximum suppression (NMS) algorithm is replaced by the distance intersection over union nonmaximum suppression (DIoU-NMS) algorithm, which improves the detection accuracy of multiple damages in the same image with a mean average precision (mAP) value of 0.934. Then, a comparative experiment with U-Net, the unimproved Mask R-CNN, MSNet and the unsupervised domain adaptation network (UDA) is carried out to verify the effectiveness of the proposed model. And combined with the segmentation and measurement results, the damage is quantitatively evaluated. Moreover, a webcam damage detection system combined with a workstation and an automatic damage detection system for smartphones is developed to quickly detect multiple types of pavement damage. In addition, on- site experiments are carried out on real pavements to verify the feasibility and effectiveness of the proposed method.