OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR
Accurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locating is challenging with limited adaptability to thei...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1554 |
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| author | Congde Lu Senguo Cao Xiao Wang Guanglai Jin Siqi Wang Wenlong Cai |
| author_facet | Congde Lu Senguo Cao Xiao Wang Guanglai Jin Siqi Wang Wenlong Cai |
| author_sort | Congde Lu |
| collection | DOAJ |
| description | Accurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locating is challenging with limited adaptability to their large-scale variations, which significantly weakens the detection performance. This study proposed a novel automatic detection network based on YOLOv5s to detect interlayer distresses in asphalt pavement named OEM-HWNet. Firstly, an object enhancement module based on prior knowledge was designed to locate the regions of interlayer distress and enhance their characteristics. Then, wavelet convolution was added to increase the receptive field of the network and enhance the ability to capture low-frequency information. Finally, an additional detection head was added to improve the detection capability of interlayer distress with different sizes. Experiments demonstrated that the proposed network achieves a mean average precision (mAP) of 89.6%, outperforming other advanced models, such as YOLOv5s, YOLOv8s, YOLOv11s, and Faster R-CNN. Incorporating prior knowledge into deep learning networks could provide an effective solution to detect interlayer distress of asphalt pavement. |
| format | Article |
| id | doaj-art-d22006f53e3c440c943fb10bb02a4564 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d22006f53e3c440c943fb10bb02a45642025-08-20T02:31:16ZengMDPI AGRemote Sensing2072-42922025-04-01179155410.3390/rs17091554OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPRCongde Lu0Senguo Cao1Xiao Wang2Guanglai Jin3Siqi Wang4Wenlong Cai5School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, ChinaJiangsu Sinoroad Engineering Technology Research Institute Co., Ltd., Nanjing 211800, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaJiangsu Sinoroad Engineering Technology Research Institute Co., Ltd., Nanjing 211800, ChinaAccurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locating is challenging with limited adaptability to their large-scale variations, which significantly weakens the detection performance. This study proposed a novel automatic detection network based on YOLOv5s to detect interlayer distresses in asphalt pavement named OEM-HWNet. Firstly, an object enhancement module based on prior knowledge was designed to locate the regions of interlayer distress and enhance their characteristics. Then, wavelet convolution was added to increase the receptive field of the network and enhance the ability to capture low-frequency information. Finally, an additional detection head was added to improve the detection capability of interlayer distress with different sizes. Experiments demonstrated that the proposed network achieves a mean average precision (mAP) of 89.6%, outperforming other advanced models, such as YOLOv5s, YOLOv8s, YOLOv11s, and Faster R-CNN. Incorporating prior knowledge into deep learning networks could provide an effective solution to detect interlayer distress of asphalt pavement.https://www.mdpi.com/2072-4292/17/9/1554interlayer distressground penetrating radarobject detectionprior knowledgeCanny operator |
| spellingShingle | Congde Lu Senguo Cao Xiao Wang Guanglai Jin Siqi Wang Wenlong Cai OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR Remote Sensing interlayer distress ground penetrating radar object detection prior knowledge Canny operator |
| title | OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR |
| title_full | OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR |
| title_fullStr | OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR |
| title_full_unstemmed | OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR |
| title_short | OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR |
| title_sort | oem hwnet a prior knowledge guided network for pavement interlayer distress detection based on computer vision using gpr |
| topic | interlayer distress ground penetrating radar object detection prior knowledge Canny operator |
| url | https://www.mdpi.com/2072-4292/17/9/1554 |
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