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#6892. Pixel-level pavement crack detection using enhanced high-resolution semantic network
| January 2027 | publication date |
| Proposal available till | 29-06-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Civil and Structural Engineering; Mechanics of Materials; |
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Abstract:
Pixel-level crack detection is crucial in pavement performance assessment. Current deep learning-based detection methods first encode input images by multi-scale feature maps, then decode them to the output that has the same size as input. This process will lose detailed crack information. To tackle this problem, this paper proposed a novel network architecture, Enhanced High-Resolution Semantic Network (EHRS-Net), to maintain and enhance detailed information of the feature maps through convolution procedure, thus, improving the overall crack detection accuracy. The contributions of this paper are: (1) Proposed Resolution Maintain Flow (RMF), which is featured by three different semantic representation extraction flows in parallel with semantic information exchange; (2) Proposed Stacked Atrous Spatial Pyramid Pooling (SASPP) module to enhance the output of the semantic features; (3) Developed a new hybrid loss function to fit proposed network architecture. The proposed methods are evaluated on two pavement crack datasets: an expanded public crack forest dataset (CFD-ex) and a new dataset called HRSD (high-resolution semantic dataset). Comprehensive comparative experiments proved the superiority of the proposed method for pavement crack detection (93.353 % mPA (mean pixel accuracy) and 78.328% mIoU (mean intersection over union) on CFD-ex; 77.159% mIoU on HRSD), especially for tiny cracks and noised pavement cracks.
Keywords:
deep learning; high-resolution semantic; image semantic segmentation; Pavement cracks; pixel-level detection
Contacts :
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Abstract:
Pixel-level crack detection is crucial in pavement performance assessment. Current deep learning-based detection methods first encode input images by multi-scale feature maps, then decode them to the output that has the same size as input. This process will lose detailed crack information. To tackle this problem, this paper proposed a novel network architecture, Enhanced High-Resolution Semantic Network (EHRS-Net), to maintain and enhance detailed information of the feature maps through convolution procedure, thus, improving the overall crack detection accuracy. The contributions of this paper are: (1) Proposed Resolution Maintain Flow (RMF), which is featured by three different semantic representation extraction flows in parallel with semantic information exchange; (2) Proposed Stacked Atrous Spatial Pyramid Pooling (SASPP) module to enhance the output of the semantic features; (3) Developed a new hybrid loss function to fit proposed network architecture. The proposed methods are evaluated on two pavement crack datasets: an expanded public crack forest dataset (CFD-ex) and a new dataset called HRSD (high-resolution semantic dataset). Comprehensive comparative experiments proved the superiority of the proposed method for pavement crack detection (93.353 % mPA (mean pixel accuracy) and 78.328% mIoU (mean intersection over union) on CFD-ex; 77.159% mIoU on HRSD), especially for tiny cracks and noised pavement cracks.
Keywords:
deep learning; high-resolution semantic; image semantic segmentation; Pavement cracks; pixel-level detection
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