Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

Abdellatif, Mohamed; Peel, Harriet; Cohn, Anthony G.; Fuentes, Raúl

doi:10.3390/rs12183084

Cited by 21 publications

(12 citation statements)

References 39 publications

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“…The process continues over a number of training epochs with the generator learning to deceive the discriminator while minimizing the loss between the generated image and the target image. Figure 3 Draw a minibatch of samples {X AB (1) ,..., x AB (m) } from domain X…”

Section: Icga Modelmentioning

confidence: 99%

“…For accurate comparison with our results, we ran experiments using the same equipment and platform environment, as well as both the Dashboard image dataset and the Roadway fractures dataset. We employed evaluation criteria such as positive predictive value (PPV) [37], true positive rate (TPR), F1, and Hausdorff distance score (HD-score) [30,38] to assess the accuracy of crack detection, since many related researchers employed these criteria to evaluate the performance of the proposed algorithms [1,3,[9][10][11]15,16,19,21]. Moreover, we will employ more criteria to evaluate the performance of our method in future studies.…”

Section: Evaluation Metricmentioning

confidence: 99%

“…Unproportioned usage of the roads in different parts of the city or rural areas influences the deterioration speed of a road surface. Cracks in the road surface are one of many warnings that roads require reconstruction [1]. The severity of this deterioration has a negative impact on the vehicles using it.…”

Section: Introductionmentioning

confidence: 99%

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Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

Kyslytsyna

Xia

Kislitsyn³

et al. 2021

Sensors

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Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.

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Section: Icga Modelmentioning

confidence: 99%

Section: Evaluation Metricmentioning

confidence: 99%

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Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

Kyslytsyna

Xia

Kislitsyn³

et al. 2021

Sensors

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show abstract

“…By 2020, the total mileage of China's highway network reached 5,198,000 km, of which 95% has been paved [1,2]. For practical engineering applications, the pavement management department needs to examine a large number of road miles [3]. At present, some nondestructive testing techniques have made some progress in pavement crack detection.…”

Section: Introductionmentioning

confidence: 99%

RIIAnet: A Real-Time Segmentation Network Integrated with Multi-Type Features of Different Depths for Pavement Cracks

Yong

Wang

2022

Applied Sciences

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Considerable road mileage puts tremendous pressure on pavement crack detection and maintenance. In practice, using a small parameter model for fast and accurate image-based crack segmentation is a challenge. However, current mainstream convolutional neural networks allocate computing resources to the same type of operators, which ignores the impact of different levels of feature extractors on the model performance. In this research, an end-to-end real-time pavement crack segmentation network (RIIAnet) is designed to improve performance by deploying different types of operators in separate layers of the network structure. Based on the extraction characteristics of cracks by convolution, involution, and asymmetric convolution, in the shallow layers the crack segmentation task is matched to extract rich low-level features by the designed asymmetric convolution enhancement module (ACE). Meanwhile, in the deep layers, the designed residual expanded involution module (REI) is used to enhance the high-level semantic features. Furthermore, the existing involution operator that fails to converge during training is improved. The ablation experiment demonstrates that the optimal ratio of the convolution and REI is 1/3 to obtain the optimal resource allocation and ACE improves the performance of the model. Especially compared with seven classical deep learning models of different structures, the results show that the proposed model reaches the highest MIOU, MPA, Recall, and F1 score of 0.7705, 0.9868, 0.8047, and 0.8485, respectively. More importantly, the parameter size of the proposed model is dramatically reduced, which is 0.04 times that of U-Net. In practice, the proposed model can be implemented in images with a high resolution of 2048 × 1024 in real time.

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“…Hyperspectral cameras measure the radiation arriving at a sensor with high spectral resolution over a sufficiently broad spectral band such that the acquired spectrum can be used to uniquely characterize and identify any given material [1]. Hyperspectral imaging plays an important role in remote sensing and has been used in a wide array of applications, such as the identification of various minerals in mining and oil industries [2], monitoring the development and health of crops in agriculture [3], detecting the development of cracks in pavements in civil engineering [4], and so on.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Zhuang

2021

Remote Sensing

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The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

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Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

Cited by 21 publications

References 39 publications

Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

RIIAnet: A Real-Time Segmentation Network Integrated with Multi-Type Features of Different Depths for Pavement Cracks

Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

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