MSK-UNET: A Modified U-Net Architecture Based on Selective Kernel with Multi-Scale Input for Pavement Crack Detection

Jiang, Xiaoliang; Jiang, Jinyun; Yu, Jianping; Wang, Jun; Wang, Ban

doi:10.1142/s0218126623500068

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Different arrows represent distinct operations. The left segment focuses on feature extraction, while the right segment involves upsampling, forming an encoder-decoder structure [37].…”

Section: Automated Identification Of Thaw Slumpingmentioning

confidence: 99%

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Li,

Dong,

Wang

et al. 2024

Sustainability

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Climate warming has induced the thawing of permafrost, which increases the probability of thaw slump occurrences in permafrost regions of the Qinghai–Tibet Engineering Corridor (QTEC). As a key and important corridor, thaw slump distribution is widespread, but research into effectively using neural networks to predict thaw slumping remains insufficient. This study automated the identification of thaw slumps within the QTEC and investigated their environmental factors and susceptibility assessment. We applied a deep learning-based semantic segmentation method, combining U-Net with ResNet101, to high spatial and temporal resolution images captured by the Gaofen-1 images. This methodology enabled the automatic delineation of 455 thaw slumps within the corridor area, covering 40,800 km², with corresponding precision, recall, and F1 scores of 0.864, 0.847, and 0.856, respectively. Subsequently, employing a radial basis function neural network model on this inventory of thaw slumps, we investigated environmental factors that could precipitate the occurrence of thaw slumps and generated sensitivity maps of thaw slumps along the QTEC. The model demonstrated high accuracy, and the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve reached 0.95. The findings of the study indicate that these thaw slumps are predominantly located on slopes with gradients of 1–18°, distributed across mid-elevation regions ranging from 4500 to 5500 m above sea level. Temperature and precipitation were identified as the predominant factors that influenced the distribution of thaw slumps. Approximately 30.75% of the QTEC area was found to fall within high to extremely high susceptibility zones. Moreover, validation processes confirmed that 82.75% of the thaw slump distribution was located within areas of high or higher sensitivity within the QTEC.

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“…Different arrows represent distinct operations. The left segment focuses on feature extraction, while the right segment involves upsampling, forming an encoder-decoder structure [37].…”

Section: Automated Identification Of Thaw Slumpingmentioning

confidence: 99%

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Li,

Dong,

Wang

et al. 2024

Sustainability

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“…Hac et al [17] proposed the use of Fast R-CNN for crack detection, and Djenouri et al [18] proposed a crack detection scheme using the scale invariant feature transformation (SIFT) algorithm to analyze the correlation between features to generate a series of graphs that are trained using a graph convolutional neural network and supervised using a super optimization algorithm. Jiang et al [19] proposed an extended version of the U-Net framework, named MSK-UNet, for crack detection. They introduced selective kernel (SK) units to replace the standard convolution blocks in the U-shaped network to obtain receptive fields with different scales.…”

Section: Highway Crack Detectionmentioning

confidence: 99%

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Zhao,

Zhou,

Wang

et al. 2023

Applied Sciences

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Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.

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“…Experiments show that the U-Net based models could obtain a higher accuracy than traditional CNN models. In addition, Jiang [15] presented an extended version of the U-Net framework, named MSK-UNet. In his work, firstly, the UNet base model is used as the basic framework to extract hierarchical features.…”

Section: Related Workmentioning

confidence: 99%

PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor

Zhang,

Huang

2023

Applied Sciences

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Crack detection plays a vital role in concrete surface maintenance. Deep-learning-based methods have achieved state-of-the-art results. However, these methods have some drawbacks. Firstly, a single-sized convolutional kernel in crack image segmentation tasks may result in feature information loss for small cracks. Secondly, only using linear interpolation or up-sampling to restore high-resolution features does not restore global information. Thirdly, these models are limited to learning edge features, causing edge feature information loss. Finally, various stains interfere with crack feature extraction. To solve these problems, a pyramid hierarchical convolution module (PHCM) is proposed by us to extract the features of cracks with different sizes. Furthermore, a mixed global attention module (MGAM) was used to fuse global feature information. Furthermore, an edge feature extractor module (EFEM) was designed by us to learn the edge features of cracks. In addition, a supplementary attention module (SAM) was used to resolv interference in stains in crack images. Finally, a pyramid hierarchical-convolution-based U-Net (PHCNet) with MGAM, EFEM, and SAM is proposed. The experimental results show that our PHCNet achieves accuracies of 0.929, 0.823, 0.989, and 0.801 on the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which is higher than that of the traditional convolutional models.

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MSK-UNET: A Modified U-Net Architecture Based on Selective Kernel with Multi-Scale Input for Pavement Crack Detection

Cited by 9 publications

References 35 publications

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

PHCNet: Pyramid Hierarchical-Convolution-Based U-Net for Crack Detection with Mixed Global Attention Module and Edge Feature Extractor

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