Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10436091/ |
| _version_ | 1797296338025578496 |
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| author | Niannian Wang Zexi Zhang Haobang Hu Bin Li Jianwei Lei |
| author_facet | Niannian Wang Zexi Zhang Haobang Hu Bin Li Jianwei Lei |
| author_sort | Niannian Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:03:11Z |
| format | Article |
| id | doaj.art-5c0adab3434c42bda6af3b4302bc8b2f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T22:03:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5c0adab3434c42bda6af3b4302bc8b2f2024-02-24T00:01:07ZengIEEEIEEE Access2169-35362024-01-0112258882590510.1109/ACCESS.2024.336595910436091Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8Niannian Wang0https://orcid.org/0000-0003-4879-6936Zexi Zhang1https://orcid.org/0009-0009-0400-7465Haobang Hu2https://orcid.org/0000-0001-7280-7442Bin Li3Jianwei Lei4https://orcid.org/0000-0002-1066-2302Yellow River Laboratory, Zhengzhou University, Zhengzhou, ChinaYellow River Laboratory, Zhengzhou University, Zhengzhou, ChinaYellow River Laboratory, Zhengzhou University, Zhengzhou, ChinaYellow River Laboratory, Zhengzhou University, Zhengzhou, ChinaYellow River Laboratory, Zhengzhou University, Zhengzhou, ChinaGround 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.https://ieeexplore.ieee.org/document/10436091/Ground penetrating radar (GPR)object detectionperceptual hashing (Phash) underground defectsYOLOv8 |
| spellingShingle | Niannian Wang Zexi Zhang Haobang Hu Bin Li Jianwei Lei Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 IEEE Access Ground penetrating radar (GPR) object detection perceptual hashing (Phash) underground defects YOLOv8 |
| title | Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 |
| title_full | Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 |
| title_fullStr | Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 |
| title_full_unstemmed | Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 |
| title_short | Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8 |
| title_sort | underground defects detection based on gpr by fusing simple linear iterative clustering phash slic phash and convolutional block attention module cbam yolov8 |
| topic | Ground penetrating radar (GPR) object detection perceptual hashing (Phash) underground defects YOLOv8 |
| url | https://ieeexplore.ieee.org/document/10436091/ |
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