Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Prashant Kumar [verfasserIn] Apurva Sharma [verfasserIn] Solomon Raju Kota [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 90330-90345
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:90330-90345

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3090961

Katalog-ID:	DOAJ019944977

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allfields	10.1109/ACCESS.2021.3090961 doi (DE-627)DOAJ019944977 (DE-599)DOAJ74173f1d6da340b2ad720f6b787a6811 DE-627 ger DE-627 rakwb eng TK1-9971 Prashant Kumar verfasserin aut Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description Electrical engineering. Electronics. Nuclear engineering Apurva Sharma verfasserin aut Solomon Raju Kota verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 90330-90345 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:90330-90345 https://doi.org/10.1109/ACCESS.2021.3090961 kostenfrei https://doaj.org/article/74173f1d6da340b2ad720f6b787a6811 kostenfrei https://ieeexplore.ieee.org/document/9461234/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 90330-90345
spelling	10.1109/ACCESS.2021.3090961 doi (DE-627)DOAJ019944977 (DE-599)DOAJ74173f1d6da340b2ad720f6b787a6811 DE-627 ger DE-627 rakwb eng TK1-9971 Prashant Kumar verfasserin aut Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description Electrical engineering. Electronics. Nuclear engineering Apurva Sharma verfasserin aut Solomon Raju Kota verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 90330-90345 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:90330-90345 https://doi.org/10.1109/ACCESS.2021.3090961 kostenfrei https://doaj.org/article/74173f1d6da340b2ad720f6b787a6811 kostenfrei https://ieeexplore.ieee.org/document/9461234/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 90330-90345
allfields_unstemmed	10.1109/ACCESS.2021.3090961 doi (DE-627)DOAJ019944977 (DE-599)DOAJ74173f1d6da340b2ad720f6b787a6811 DE-627 ger DE-627 rakwb eng TK1-9971 Prashant Kumar verfasserin aut Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description Electrical engineering. Electronics. Nuclear engineering Apurva Sharma verfasserin aut Solomon Raju Kota verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 90330-90345 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:90330-90345 https://doi.org/10.1109/ACCESS.2021.3090961 kostenfrei https://doaj.org/article/74173f1d6da340b2ad720f6b787a6811 kostenfrei https://ieeexplore.ieee.org/document/9461234/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 90330-90345
allfieldsGer	10.1109/ACCESS.2021.3090961 doi (DE-627)DOAJ019944977 (DE-599)DOAJ74173f1d6da340b2ad720f6b787a6811 DE-627 ger DE-627 rakwb eng TK1-9971 Prashant Kumar verfasserin aut Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description Electrical engineering. Electronics. Nuclear engineering Apurva Sharma verfasserin aut Solomon Raju Kota verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 90330-90345 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:90330-90345 https://doi.org/10.1109/ACCESS.2021.3090961 kostenfrei https://doaj.org/article/74173f1d6da340b2ad720f6b787a6811 kostenfrei https://ieeexplore.ieee.org/document/9461234/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 90330-90345
allfieldsSound	10.1109/ACCESS.2021.3090961 doi (DE-627)DOAJ019944977 (DE-599)DOAJ74173f1d6da340b2ad720f6b787a6811 DE-627 ger DE-627 rakwb eng TK1-9971 Prashant Kumar verfasserin aut Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images. Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description Electrical engineering. Electronics. Nuclear engineering Apurva Sharma verfasserin aut Solomon Raju Kota verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 90330-90345 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:90330-90345 https://doi.org/10.1109/ACCESS.2021.3090961 kostenfrei https://doaj.org/article/74173f1d6da340b2ad720f6b787a6811 kostenfrei https://ieeexplore.ieee.org/document/9461234/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 90330-90345
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topic_title	TK1-9971 Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model Understanding the mask R-CNN framework for instance segmentation of concrete damage mask R-CNN model description
topic	misc TK1-9971 misc Understanding the mask R-CNN misc framework for instance segmentation of concrete damage misc mask R-CNN model description misc Electrical engineering. Electronics. Nuclear engineering
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title	Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model
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title_sort	automatic multiclass instance segmentation of concrete damage using deep learning model
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title_auth	Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model
abstract	Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.
abstractGer	Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.
abstract_unstemmed	Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that need to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.
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title_short	Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model
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Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

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