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Classification and Pixel-Level Segmentation of Asphalt Pavement Cracks Using Convolutional Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Classification and Pixel-Level Segmentation of Asphalt Pavement Cracks Using Convolutional Neural Networks./
Author:	Adnan, Tamim.
Description:	1 online resource (125 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380375078

Classification and Pixel-Level Segmentation of Asphalt Pavement Cracks Using Convolutional Neural Networks.
Adnan, Tamim.

Classification and Pixel-Level Segmentation of Asphalt Pavement Cracks Using Convolutional Neural Networks. - 1 online resource (125 pages)

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2023.

Includes bibliographical references

This study was conducted to answer two research questions. How do the chosen CNN models perform on publicly available asphalt pavement crack datasets for classification and segmentation? And how can these CNN models be fine-tuned to improve their performance? For classification, a ResNet50 model with transfer learning was explored, using eight different epochs and two optimizers, a total of sixteen combinations, and the optimal combination of these two hyperparameters was identified. On the other hand, the segmentation task was accomplished using a modified-UNet model on two different datasets, and the results were presented and discussed.The dataset for classification was derived from a publicly available dataset, EdmCrack600. It was given the name AugCrack132. It has three principal types of asphalt pavement cracks: alligator, longitudinal, and transverse. The training dataset consists of 132 ground truth images, and the testing dataset has 56 raw crack images. The optimal classification accuracy occurred at epoch 500 with the 'adaptive moment estimation' or "Adam" optimizer algorithm, while the least accuracy occurred at epoch 40 with 'stochastic gradient descent' or "SGD" optimizer algorithm. The classification accuracy on the overall dataset varied from 8% to 58%, the F1 score was from 1.463% to 59%, the precision ranged from less than 1% to 68%, and the recall varied from 8.9% to 59%.The modified-UNet model was trained and tested on two published pavement crack datasets to segment asphalt pavement cracks. The first dataset included 470 images and corresponding masks obtained from the EdmCrack600 dataset through a preprocessing, and it was named EdmCrack470. Furthermore, 206 images from the CRACKTREE260 dataset were used to evaluate the modified-UNet model, as a result, the dataset has been named CRACTREE206 here. At epoch 30, the model achieved an IoU of 67%, precision 96%, recall 65%, and F1 78% score for the EdmCrack470 dataset. Moreover, the values of the evaluation metrics for CRACKTREE206 are: IoU 60%, precision 95%, recall 58%, and F1 72%. In addition, the predicted masks were assessed based on three criteria.The research highlights that transferred-ResNet50 has successfully classified the pavement crack types in some hyperparameter combinations. Hence, this model should be applied to a more organized classification dataset where ground truths of the cracks need to be more specific with severity levels. Also, this study recommended considering more hyperparameter combinations to evaluate the model performance for classification tasks.Furthermore, the modified-UNet model could contribute to pavement crack segmentation. The addition of multi-scale layers in both encoder-decoder networks can be used to improve the performance. This inclusion will assist the model in differentiating the cracks from noises and redundant background information. Also, some additional data preprocessing and cleaning are the potential tasks to improve the accuracy of the predicted shapes.From this study, it is clear that state-of-the-art CNN models can be used for evaluating both new and existing pavement crack datasets. Creating a new model for specific datasets is challenging because the biased parameters of the model would be less effective for another dataset. Also, it can raise complexities when the model framework is simple or complicated compared to the data size and structure. Therefore, fine-tuning the existing models can be more productive in pavement crack classification and segmentation tasks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380375078Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Classification tasksIndex Terms--Genre/Form:

554714
Electronic books.

Classification and Pixel-Level Segmentation of Asphalt Pavement Cracks Using Convolutional Neural Networks.
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520 $a This study was conducted to answer two research questions. How do the chosen CNN models perform on publicly available asphalt pavement crack datasets for classification and segmentation? And how can these CNN models be fine-tuned to improve their performance? For classification, a ResNet50 model with transfer learning was explored, using eight different epochs and two optimizers, a total of sixteen combinations, and the optimal combination of these two hyperparameters was identified. On the other hand, the segmentation task was accomplished using a modified-UNet model on two different datasets, and the results were presented and discussed.The dataset for classification was derived from a publicly available dataset, EdmCrack600. It was given the name AugCrack132. It has three principal types of asphalt pavement cracks: alligator, longitudinal, and transverse. The training dataset consists of 132 ground truth images, and the testing dataset has 56 raw crack images. The optimal classification accuracy occurred at epoch 500 with the 'adaptive moment estimation' or "Adam" optimizer algorithm, while the least accuracy occurred at epoch 40 with 'stochastic gradient descent' or "SGD" optimizer algorithm. The classification accuracy on the overall dataset varied from 8% to 58%, the F1 score was from 1.463% to 59%, the precision ranged from less than 1% to 68%, and the recall varied from 8.9% to 59%.The modified-UNet model was trained and tested on two published pavement crack datasets to segment asphalt pavement cracks. The first dataset included 470 images and corresponding masks obtained from the EdmCrack600 dataset through a preprocessing, and it was named EdmCrack470. Furthermore, 206 images from the CRACKTREE260 dataset were used to evaluate the modified-UNet model, as a result, the dataset has been named CRACTREE206 here. At epoch 30, the model achieved an IoU of 67%, precision 96%, recall 65%, and F1 78% score for the EdmCrack470 dataset. Moreover, the values of the evaluation metrics for CRACKTREE206 are: IoU 60%, precision 95%, recall 58%, and F1 72%. In addition, the predicted masks were assessed based on three criteria.The research highlights that transferred-ResNet50 has successfully classified the pavement crack types in some hyperparameter combinations. Hence, this model should be applied to a more organized classification dataset where ground truths of the cracks need to be more specific with severity levels. Also, this study recommended considering more hyperparameter combinations to evaluate the model performance for classification tasks.Furthermore, the modified-UNet model could contribute to pavement crack segmentation. The addition of multi-scale layers in both encoder-decoder networks can be used to improve the performance. This inclusion will assist the model in differentiating the cracks from noises and redundant background information. Also, some additional data preprocessing and cleaning are the potential tasks to improve the accuracy of the predicted shapes.From this study, it is clear that state-of-the-art CNN models can be used for evaluating both new and existing pavement crack datasets. Creating a new model for specific datasets is challenging because the biased parameters of the model would be less effective for another dataset. Also, it can raise complexities when the model framework is simple or complicated compared to the data size and structure. Therefore, fine-tuning the existing models can be more productive in pavement crack classification and segmentation tasks.
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653 $a Crack segmentation
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