Data Augmentation and Intelligent Recognition in Pavement Texture Using a Deep Learning

Chen, Ning; Xu, Zijin; Liu, Zhuo; Chen, Yihan; Miao, Yinghao; Li, Qiuhan; Hou, Yue; Wang, Linbing

doi:10.1109/tits.2022.3140586

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…The corresponding schematic is shown in Figure 4. Figure 3c demonstrates the effectiveness of the texture data augmentation method, which can also be extended to other texture‐related studies (Chen et al., 2022; Jiang et al., 2018); hence, the number of specimens prepared in the laboratory could be reduced.…”

Section: Texture Data Preprocessingmentioning

confidence: 78%

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Pan

Ren

et al. 2023

Computer aided Civil Eng

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The high cost of collecting surficial textures has been the bottleneck problem for many decades. To bridge this gap, this study aims to propose a complete framework based on the generative adversarial networks (GANs) architecture for texture regeneration using limited texture data samples. Four variants of the GAN were compared to achieve the best regeneration performance. Prior to running GAN, specifically improved data augmentation and modified sigmoid normalization operations were dedicated to avoiding the loss of texture information and the non‐convergence problem. After running GAN, the estimation metrics were introduced and discussed separately to quantitatively evaluate the rationality and diversity of the regenerated textures. The results demonstrated that the use of improved data augmentation and normalization methods significantly enhanced the regeneration capability of GAN, and more importantly the skewed distribution of surficial heights which is not achievable by present texture regeneration methods was most effectively regenerated by deep regret analytic GAN with well generalization ability. Our study provides a promising choice to generate virtually realistic pavement surface textures.

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Section: Texture Data Preprocessingmentioning

confidence: 78%

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Pan

Ren

et al. 2023

Computer aided Civil Eng

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“…The classification task involves identifying four of the most common types of pavement texture: dense asphalt concrete (DAC), micro surface (MS), open-graded friction course (OGFC) and stone matrix asphalt (SMA). Typical images of each type of pavement texture refer to [11]. In The proposed few-shot learning models were evaluated using a four-way five-shot classification task, which is typical in the field of few-shot learning [42][43][44][45].…”

Section: (A) Dataset Of Pavement Texturementioning

confidence: 99%

“…Specifically, for pavement texture recognition, deep learning has shown excellent performance. Chen et al [11] applied the Dense Convolutional Network to classify pavement texture by generating adversarial networks to expand pavement texture, demonstrating the superiority of deep learning over manual classification and traditional machine learning methods. Liu et al [12] proposed a precise and stable framework for pavement texture measurement and reconstruction, using a depth CNN-based encoder to extract features from pavement images and converting them into models using feature mapping units and decoders.…”

Section: Introductionmentioning

confidence: 99%

Automatic pavement texture recognition using lightweight few-shot learning

Shuo

Yan

Liu

et al. 2023

Phil. Trans. R. Soc. A.

Self Cite

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Texture is a crucial characteristic of roads, closely related to their performance. The recognition of pavement texture is of great significance for road maintenance professionals to detect potential safety hazards and carry out necessary countermeasures. Although deep learning models have been applied for recognition, the scarcity of data has always been a limitation. To address this issue, this paper proposes a few-shot learning model based on the Siamese network for pavement texture recognition with a limited dataset. The model achieved 89.8% accuracy in a four-way five-shot task classifying the pavement textures of dense asphalt concrete, micro surface, open-graded friction course and stone matrix asphalt. To align with engineering practice, global average pooling (GAP) and one-dimensional convolution are implemented, creating lightweight models that save storage and training time. Comparative experiments show that the lightweight model with GAP implemented on dense layers and one-dimensional convolution on convolutional layers reduced storage volume by 94% and training time by 99%, despite a 2.9% decrease in classification accuracy. Moreover, the model with only GAP implemented on dense layers achieved the highest accuracy at 93.5%, while reducing storage volume and training time by 83% and 6%, respectively. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

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“…The augmentation method using affine transformations allows you to increase the amount of training data due to minor changes in the color, size, and shape of images [41]. However, simple operations are not enough to significantly increase the accuracy of recognition of the minority category or overcome the problem of overfitting [42]. The resampling method balances the training data and can be used as oversampling in minority categories [43], undersampling in majority categories [44], or as a combination of both methods [45].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function

Lyakhov¹,

Lyakhova²,

Kalita³

2023

Preprint

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Currently, skin cancer is the most commonly diagnosed form of cancer in humans and is one of the leading causes of death in patients with cancer. Biopsy methods are an invasive research method and are not always available for primary diagnosis. Imaging methods have low accuracy and depend on the experience of the dermatologist. Artificial intelligence technologies can match and surpass visual analysis methods in accuracy, but they have the risk of a false negative response when a malignant pigmented lesion can be recognized as benign. One possible way to improve accuracy and reduce the risk of false negatives is to analyze heterogeneous data, combine different preprocessing methods, and use modified loss functions to eliminate the negative impact of unbalanced dermatological data. The paper proposes a multimodal neural network system with a modified cross-entropy loss function that is sensitive to unbalanced heterogeneous dermatological data. The accuracy of recognition in 10 diagnostically significant categories for the proposed system was 85.19%. The novelty of the proposed system lies in the use of cross-entropy loss when training the modified function with the help of weight coefficients. The introduction of weighting factors has reduced the number of false negative forecasts, as well as improved accuracy by 1.02-4.03 percentage points compared to the original multimodal systems. The introduction of the proposed multimodal system as an auxiliary diagnostic tool can reduce the consumption of financial and labor resources involved in the medical industry, as well as increase the chance of early detection of skin cancer.

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Data Augmentation and Intelligent Recognition in Pavement Texture Using a Deep Learning

Cited by 24 publications

References 28 publications

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

Automatic pavement texture recognition using lightweight few-shot learning

Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function

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