Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost
https://orcid.org/0000-0002-5894-5969
Highlights Pavement raveling detection based on image processing is proposed. Texture analysis techniques are employed for feature extraction. Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. XGBoost classifier is employed for the classification of raveling and no-raveling images. The results of LBP-GLCM feature extraction achieved better performance for raveling detection.
Abstract The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model’s generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities.
Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost
https://orcid.org/0000-0002-5894-5969
Highlights Pavement raveling detection based on image processing is proposed. Texture analysis techniques are employed for feature extraction. Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. XGBoost classifier is employed for the classification of raveling and no-raveling images. The results of LBP-GLCM feature extraction achieved better performance for raveling detection.
Abstract The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model’s generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities.
Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost
https://orcid.org/0000-0002-5894-5969
Highlights Pavement raveling detection based on image processing is proposed. Texture analysis techniques are employed for feature extraction. Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. XGBoost classifier is employed for the classification of raveling and no-raveling images. The results of LBP-GLCM feature extraction achieved better performance for raveling detection.
Abstract The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model’s generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities.
Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost
Daneshvari, Mohammad Hassan (Autor:in) / Nourmohammadi, Ebrahim (Autor:in) / Ameri, Mahmoud (Autor:in) / Mojaradi, Barat (Autor:in)
31.07.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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