Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8

• Published in: IEEE access 2024, Vol.12, p.25888-25905
• Main Authors: Wang, Niannian, Zhang, Zexi, Hu, Haobang, Li, Bin, Lei, Jianwei
• Format: Article
• Language: English
• Subjects: Algorithms; Buried object detection; Clustering; Clustering algorithms; Computational modeling; Convolutional neural networks; Data augmentation; Data models; Datasets; Deep learning; Defects; Ground penetrating radar; Ground penetrating radar (GPR); Iterative methods; Machine learning; Modules; Nondestructive testing; Object detection; Optimization; perceptual hashing (Phash) underground defects; Target detection; Training; YOLO; YOLOv8
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3365959

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.