ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation

Accurate crack detection is crucial for maintaining pavement integrity, yet manual inspections remain labor-intensive and prone to errors, underscoring the need for automated solutions. This study proposes a novel crack segmentation approach utilizing advanced visual models, specifically Detectron2 and the Segment Anything Model (SAM), applied to the CFD and Crack500 datasets, which exhibit intricate and diverse crack patterns. Detectron2 was tested with four configurations—mask_rcnn_R_50_FPN_3x, mask_rcnn_R_101_FPN_3x, faster_rcnn_R_50_FPN_3x, and faster_rcnn_R_101_FPN_3x—while SAM was compared using Focal Loss, DiceCELoss, and DiceFocalLoss. SAM with DiceFocalLoss outperformed Detectron2, achieving mean IoU scores of 0.69 and 0.59 on the CFD and Crack500 datasets, respectively. The integration of Detectron2 with faster_rcnn_R_101_FPN_3x and SAM using DiceFocalLoss involves generating bounding boxes with Detectron2, which serve as prompts for SAM to produce segmentation masks. This approach achieves mIoU scores of 0.83 for CFD dataset and 0.75 for Crack500 dataset. These results highlight the potential of combining foundation models with Detectron2 for advancing crack detection technologies, offering valuable insights for enhancing highway maintenance systems.

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Crack-SAM: Crack Segmentation Using a Foundation Model

Kumar

et al. 2024

Preprint

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Ensuring the structural integrity of pavements requires precise crack detection and evaluation. Manual inspections, although essential, are labour-intensive, time-consuming, and susceptible to errors, emphasizing the need for automated visual inspection techniques. This study presents an integrated approach to crack assessment by utilizing advanced visual models such as the Detectron2 model zoo and the Segment Anything Model (SAM) on Dataset A and Dataset B, which contain images from diverse locations with complex backgrounds and varying crack structures. Experiments were conducted using the Detectron2 model with four baseline configurations (mask_rcnn_R_50_FPN_3x, mask_rcnn_R_101_FPN_3x, fast_rcnn_R_50_FPN_3x, and fast_rcnn_R_101_FPN_3x), selected for their proven performance in object detection tasks and their ability to balance computational efficiency with high detection accuracy. Additionally, SAM was fine-tuned with three loss functions (Focal Loss, DiceCELoss, and DiceFocalLoss) chosen for their effectiveness in handling class imbalance and improving segmentation accuracy. Results demonstrate that SAM fine-tuned with DiceFocalLoss outperforms Detectron2 in crack segmentation, achieving mean intersection over union (MIoU) values of 0.69 for Dataset A and 0.59 for Dataset B. The integration of Detectron2 with fast_rcnn_R_101_FPN_3x as the baseline and SAM with DiceFocalLoss involves training the Detectron2 model to generate approximate bounding boxes around objects of interest, which are then used as prompts for the SAM model to produce segmentation masks, resulting in MIoU values of 0.83 for Dataset A and 0.75 for Dataset B. These findings represent significant advancements in crack identification methods, with substantial implications for improving highway maintenance practices.

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ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation

Cited by 4 publications

References 15 publications

Pavement Crack Detection Algorithm Based on Bi-Layer Connectivity Checking

Pavement Crack Detection Algorithm Based on Bi-Layer Connectivity Checking

Crack SAM: enhancing crack detection utilizing foundation models and Detectron2 architecture

Crack-SAM: Crack Segmentation Using a Foundation Model

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