Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps
Asphalt cracking poses a significant deterioration challenge in road networks. Historically, government agencies employed visual inspection methods to identify asphalt cracking. However, this approach is labour-intensive and time-consuming, prompting most authorities to transition to automated crack...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525004115 |
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| author | Gihan P. Ruwanpathirana Sadeep Thilakarathna Shanaka Kristombu Baduge |
| author_facet | Gihan P. Ruwanpathirana Sadeep Thilakarathna Shanaka Kristombu Baduge |
| author_sort | Gihan P. Ruwanpathirana |
| collection | DOAJ |
| description | Asphalt cracking poses a significant deterioration challenge in road networks. Historically, government agencies employed visual inspection methods to identify asphalt cracking. However, this approach is labour-intensive and time-consuming, prompting most authorities to transition to automated crack identification methods utilising Artificial Intelligence (A.I.). Convolutional neural networks (CNNs) have gained popularity due to their ease of implementation and enhanced accuracy. Nevertheless, CNN models were perceived as opaque entities. In this study, we employed Integrated Gradient (I.G.) maps to elucidate the workings of these models and interpret CNN-based crack image voxels that contributed to the positive (cracked) output of CNN. Our findings revealed that CNN utilised voxels within the cracks to classify the cracked image segments and highlighted the image features that CNN employed to distinguish cracks from their surroundings. These interpretation techniques provide insights into the behaviour of the CNN model, enabling the refinement of Deep Learning (DL) models. |
| format | Article |
| id | doaj-art-3580ea8391434aee86e1d7dedb14ba65 |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-3580ea8391434aee86e1d7dedb14ba652025-08-20T03:17:44ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0461310.1016/j.cscm.2025.e04613Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) mapsGihan P. Ruwanpathirana0Sadeep Thilakarathna1Shanaka Kristombu Baduge2Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC, AustraliaCorresponding author.; Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC, AustraliaAsphalt cracking poses a significant deterioration challenge in road networks. Historically, government agencies employed visual inspection methods to identify asphalt cracking. However, this approach is labour-intensive and time-consuming, prompting most authorities to transition to automated crack identification methods utilising Artificial Intelligence (A.I.). Convolutional neural networks (CNNs) have gained popularity due to their ease of implementation and enhanced accuracy. Nevertheless, CNN models were perceived as opaque entities. In this study, we employed Integrated Gradient (I.G.) maps to elucidate the workings of these models and interpret CNN-based crack image voxels that contributed to the positive (cracked) output of CNN. Our findings revealed that CNN utilised voxels within the cracks to classify the cracked image segments and highlighted the image features that CNN employed to distinguish cracks from their surroundings. These interpretation techniques provide insights into the behaviour of the CNN model, enabling the refinement of Deep Learning (DL) models.http://www.sciencedirect.com/science/article/pii/S2214509525004115Integrated gradient mapsConvolutional neural networksArtificial IntelligenceDeep learningMachine learningAsphalt pavements |
| spellingShingle | Gihan P. Ruwanpathirana Sadeep Thilakarathna Shanaka Kristombu Baduge Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps Case Studies in Construction Materials Integrated gradient maps Convolutional neural networks Artificial Intelligence Deep learning Machine learning Asphalt pavements |
| title | Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps |
| title_full | Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps |
| title_fullStr | Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps |
| title_full_unstemmed | Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps |
| title_short | Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps |
| title_sort | interpretation and understanding of asphalt crack detection deep learning models using integrated gradient i g maps |
| topic | Integrated gradient maps Convolutional neural networks Artificial Intelligence Deep learning Machine learning Asphalt pavements |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525004115 |
| work_keys_str_mv | AT gihanpruwanpathirana interpretationandunderstandingofasphaltcrackdetectiondeeplearningmodelsusingintegratedgradientigmaps AT sadeepthilakarathna interpretationandunderstandingofasphaltcrackdetectiondeeplearningmodelsusingintegratedgradientigmaps AT shanakakristombubaduge interpretationandunderstandingofasphaltcrackdetectiondeeplearningmodelsusingintegratedgradientigmaps |