Figure - available from: Journal of Civil Structural Health Monitoring
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Four kinds of pavement anomalies with the extraction location: a loose layer; b small hole; c diagonal crack; d fault
Source publication
Highways are the main components of modern transportation hubs, where lots of pavement anomalies have been still occurring with the increasing of the traffic volume and overloading, especially for the coastal developed areas. Rapid nondestructive detection is a necessary means to ensure uninterrupted highway traffic in the road operation period. In...
Citations
... Martinez-Ríos et al. [31] used continuous wavelet transform (CWT) to convert monitored vehicle response signals and then utilized a convolutional neural network (CNN) to identify transverse pavement cracks using a transfer learning approach. Similar CWT-based research was presented by Xie et al. [32]. Wang et al. [33] proposed a framework using accelerometers and gyroscopes attached to vehicles to detect road surface defects, achieving real-time monitoring of road anomalies in Taipei city via the Internet of Things. ...
Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network’s decisions and guide measures to improve its performance. The results demonstrate that the model’s classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps.
... In recent years, GPR technology has made significant progress, including advances in data processing [7]. First in the early 21st century, owing to the ongoing growth and use of computer technology, Gamba et al. [8] employed Neural Networks (NN) to analyze GPR data. ...
Ground Penetrating Radar (GPR) is an effective non-destructive detection method, that is frequently utilized in the detection of urban underground defects because of its quick speed, convenient and flexible operation, and high resolution. However, there are some limitations to defect detection using GPR, such as less data, poor data quality, and complexity of data interpretation. In this study, an underground defect detection system based on GPR was established. First, a Simple Linear Iterative Clustering (SLIC)-PHash, a Data Augmentation (DA) optimization algorithm, was created to obtain high-quality datasets. Second, the Convolutional Block Attention Module (CBAM)-YOLOv8, a detection model, was produced for the recognition of defects. This model uses GhostConv and CBAM to create a lighter design that better focuses on target detection and increases efficiency. Finally, a one-click detection system was formed by fusing SLIC-Phsh and CBAM-YOLOv8, which were used for one-click GPR dataset optimization and defect detection. The developed system has the best detection mAP and F1 scores of 90.8% and 88.3%, respectively, compared to several well-known Deep Learning (DL)-based techniques. The results demonstrated that the system proposed in this paper can greatly improve detection efficiency and reduce detection time by achieving a good balance between detection speed and accuracy.
