The regular detection of asphalt pavement damage is crucial for the safe operation of highway. However, the current detection method has the problem of low efficiency and accuracy. In addition, the image tangential distortion will be caused by the oblique photography of the acquisition equipment. Thus, the accuracy of extracted the pothole area is affected. In order to solve the above problems, a segmentation method of asphalt pavement pothole damage based on image correction and deep learning is proposed. Firstly, an image correction method called perspective change is proposed to solve the tangential distortion problem of asphalt pavement image. Secondly, an innovative detection and segmentation model called CMSegNet is developed and used for the first time for accurate segmentation of asphalt pavement pothole damage. In this model, the global feature extraction capability of the pothole target is improved by optimizing the Mamba structure. In addition, a Multi-Scale Attention Aggregation (MSAA) module is proposed to fuse multi-scale local and global features. The segmentation accuracy, IoU, F1-Score and segmentation efficiency of this model can reach 91.28%, 89.96%, 91.74% and 24.52FPS, respectively. Finally, on-site experiments of asphalt pavement pothole detection are carried out to verify the generality, power and scalability of the proposed correction method and segmentation model. The results show that the proposed image correction method based on perspective transformation can effectively reduce the error rate of quantization of pothole area. The error rate is only 6.84%, an improvement of 4.09%. In addition, the superiority of the proposed CMSegNet segmentation model is further proved.

Rapid and accurate three-dimensional (3D) detection of asphalt pavement pothole damage is crucial for pavement performance and quality evaluation. However, existing 3D reconstruction methods based on lasers and depth cameras are expensive and lack operability. In addition‘, false potholes’ are formed through gaps between the asphalt aggregate particles and they interfere with the reconstruction of real potholes. In order to solve the above problems, a new CBAM (convolutional block attention module) optimized pothole segmentation and reconstruction system for monocular images is proposed. First, a network called CBAM-Seg-CapsNet is developed to accurately segment pothole areas from two-dimensional monocular images of pavement. The influence of ‘false potholes’ on the reconstruction of real potholes is avoided. Secondly, an unsupervised monomural depth estimation intelligent network, called ‘CBAM-Recon-Depth’, is developed to realize effective reconstruction of the segmented pothole area. The expensive problem of 3D reconstruction methods, based on lasers and depth cameras, is solved. Compared with some famous segmentation and reconstruction models, the segmentation and reconstruction accuracy of the proposed system are 96.62% and 91.32%, respectively, and the F1-score is 93.89% and 90.76%, respectively. The Dice value of segmentation is 92.27%. The reconstructed RMES Log and Abs Rel are as low as 0.1436 and 0.1252, respectively. On-site experiments are carried out and the reconstruction rate reached 96.05%. The results show that the CBAM optimization system can accurately extract the pothole area and realize the high-precision reconstruction of pothole damage after extraction.

Ground-penetrating radar (GPR) has been widely applied to the detection and delineation of buried targets in the subsurface. Compared with conventional single-channel GPR, polarimetric GPR has been proven to possess an improved ability to detect and characterize an elongated object in the subsurface. This letter proves that the scattering signals from a cylinder in two orthogonal polarization channels have a phase difference of about 90° when its diameter-to-wavelength ratio is about 0.05–0.33. Consequently, a polarization-difference imaging method, which shifts the VV component by −90° and subtracts it from the HH component, is proposed for the improved detection and imaging of a subsurface elongated object. Its effectiveness is verified by numerical, laboratory, and field tests on buried rebars and pipes. The signal-to-clutter ratios of the reconstructed GPR images can be improved by up to 4.5 dB by considering the phase difference between the dual-polarization 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). Firstly, 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.

This letter presents a ground-penetrating radar (GPR) forward model on dispersive media based on parallel conformal symplectic Euler algorithm. We developed a symplectic Euler algorithm combined with conformal meshes and graphics processing unit (GPU) acceleration technology, which proved to be an accurate and effective method to simulating GPR electromagnetic wave propagation in dispersive media.

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.

The visualization of complex physical processes is becoming a challenging topic in fields of information visualization, and attracting more and more researchers. Microstructure evolution visualization (MEV) has become an important and unsubstitutable method in the modern material processing engineering domains. A novel MEV approach combining the neutrosophic set theory (NS) and the cellular automaton technique (CA) is developed in order to precisely simulate the invisible, complex and unrepeatable physical process of metal solidification. The NS theory is applied to realize the complex evolution rules among three phases including solid phase, liquid phase and interface phase, while the CA method is used to simulate the dynamic process of dendrite growth. Experiment results of the dendrite growing simulation show strange consistency of the virtual invisible microstructure with that in practical industrial trials and production. Material experts also convince the statistical characteristics of the simulation results and further the inspirational value for the visualization of other complex physical processes.

Construction of electromagnetic wave propagation model in layered pavement structure is a key problem for applying ground penetrating radar (GPR) to the road quality detection. A first-order explicit symplectic Euler method with Higdon absorbing boundary condition is presented to simulate GPR wave propagation in 2-D pavement structure. The incident wave is considered as line source and plane wave source, respectively. The total-field/scatter-field technique is used to simulate plane wave excitation. Numerical examples are provided to verify the accuracy and efficiency of the proposed algorithm. It can be observed that the symplectic Euler method achieves almost the same level of accuracy as the finite-difference time-domain scheme, while saving CPU time considerably.