Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296968702> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W4296968702 endingPage "104575" @default.
- W4296968702 startingPage "104575" @default.
- W4296968702 abstract "Fatigue cracking is usually associated with the structural failure of asphalt pavement. This work aims to apply infrared thermography and deep learning, especially convolutional neural network (CNN), for asphalt pavement fatigue crack severity classification. A dataset of asphalt pavement fatigue cracking was built with four severity levels (no, low-severity, medium-severity, and high-severity) and three image types (visible, infrared, and fusion; fusion is the fusion of visible and infrared images). Thirteen CNN models were trained and evaluated based on accuracy, complexity (computational and model), and memory usage. This work applied Grad-CAM and Guided Grad-CAM to interpret the CNN model for classifying the different severity levels of different crack types (fatigue and longitudinal or transverse cracks) for the three image types. This work investigated the effect of image types on classifying different severity levels of fatigue crack and discussed the applicability of infrared thermography on crack detection (crack severity classification and crack segmentation). The results show that the CNN model had the highest accuracy on the infrared image, followed by the fusion image, and the lowest on the visible image. EfficientNet-B4 achieved the highest accuracy on all three image types, while the accuracy of CNN exceeded 0.95 on all three image types. Different image types made the CNN model have different accuracy in classifying the different severity levels of different crack types, which was interpreted by Grad-CAM and Guided Grad-CAM. Based on the high accuracy and reliability, the fusion image could be an accurate, efficient, and reliable method for crack detection." @default.
- W4296968702 created "2022-09-25" @default.
- W4296968702 creator A5003740227 @default.
- W4296968702 creator A5026154387 @default.
- W4296968702 creator A5060165452 @default.
- W4296968702 date "2022-11-01" @default.
- W4296968702 modified "2023-10-11" @default.
- W4296968702 title "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning" @default.
- W4296968702 cites W2023920199 @default.
- W4296968702 cites W2051844595 @default.
- W4296968702 cites W2083815608 @default.
- W4296968702 cites W2735436330 @default.
- W4296968702 cites W2773357723 @default.
- W4296968702 cites W2807725438 @default.
- W4296968702 cites W2808919226 @default.
- W4296968702 cites W2893813411 @default.
- W4296968702 cites W2910362756 @default.
- W4296968702 cites W2919115771 @default.
- W4296968702 cites W2954996726 @default.
- W4296968702 cites W2982564821 @default.
- W4296968702 cites W2990192313 @default.
- W4296968702 cites W2997035735 @default.
- W4296968702 cites W3009621818 @default.
- W4296968702 cites W3024770686 @default.
- W4296968702 cites W3033645921 @default.
- W4296968702 cites W3087277009 @default.
- W4296968702 cites W3097389829 @default.
- W4296968702 cites W3099319035 @default.
- W4296968702 cites W3112520736 @default.
- W4296968702 cites W3123535773 @default.
- W4296968702 cites W3134108147 @default.
- W4296968702 cites W3175064897 @default.
- W4296968702 cites W3195438473 @default.
- W4296968702 cites W3203911595 @default.
- W4296968702 cites W4200535558 @default.
- W4296968702 cites W4226252340 @default.
- W4296968702 cites W4281975579 @default.
- W4296968702 doi "https://doi.org/10.1016/j.autcon.2022.104575" @default.
- W4296968702 hasPublicationYear "2022" @default.
- W4296968702 type Work @default.
- W4296968702 citedByCount "4" @default.
- W4296968702 countsByYear W42969687022023 @default.
- W4296968702 crossrefType "journal-article" @default.
- W4296968702 hasAuthorship W4296968702A5003740227 @default.
- W4296968702 hasAuthorship W4296968702A5026154387 @default.
- W4296968702 hasAuthorship W4296968702A5060165452 @default.
- W4296968702 hasConcept C115961682 @default.
- W4296968702 hasConcept C120665830 @default.
- W4296968702 hasConcept C121332964 @default.
- W4296968702 hasConcept C153180895 @default.
- W4296968702 hasConcept C154945302 @default.
- W4296968702 hasConcept C158355884 @default.
- W4296968702 hasConcept C159985019 @default.
- W4296968702 hasConcept C163258240 @default.
- W4296968702 hasConcept C168056786 @default.
- W4296968702 hasConcept C192562407 @default.
- W4296968702 hasConcept C2779222261 @default.
- W4296968702 hasConcept C31972630 @default.
- W4296968702 hasConcept C41008148 @default.
- W4296968702 hasConcept C43214815 @default.
- W4296968702 hasConcept C62520636 @default.
- W4296968702 hasConcept C69744172 @default.
- W4296968702 hasConcept C81363708 @default.
- W4296968702 hasConceptScore W4296968702C115961682 @default.
- W4296968702 hasConceptScore W4296968702C120665830 @default.
- W4296968702 hasConceptScore W4296968702C121332964 @default.
- W4296968702 hasConceptScore W4296968702C153180895 @default.
- W4296968702 hasConceptScore W4296968702C154945302 @default.
- W4296968702 hasConceptScore W4296968702C158355884 @default.
- W4296968702 hasConceptScore W4296968702C159985019 @default.
- W4296968702 hasConceptScore W4296968702C163258240 @default.
- W4296968702 hasConceptScore W4296968702C168056786 @default.
- W4296968702 hasConceptScore W4296968702C192562407 @default.
- W4296968702 hasConceptScore W4296968702C2779222261 @default.
- W4296968702 hasConceptScore W4296968702C31972630 @default.
- W4296968702 hasConceptScore W4296968702C41008148 @default.
- W4296968702 hasConceptScore W4296968702C43214815 @default.
- W4296968702 hasConceptScore W4296968702C62520636 @default.
- W4296968702 hasConceptScore W4296968702C69744172 @default.
- W4296968702 hasConceptScore W4296968702C81363708 @default.
- W4296968702 hasLocation W42969687021 @default.
- W4296968702 hasOpenAccess W4296968702 @default.
- W4296968702 hasPrimaryLocation W42969687021 @default.
- W4296968702 hasRelatedWork W2175746458 @default.
- W4296968702 hasRelatedWork W2359631359 @default.
- W4296968702 hasRelatedWork W2419576664 @default.
- W4296968702 hasRelatedWork W2732542196 @default.
- W4296968702 hasRelatedWork W2767265881 @default.
- W4296968702 hasRelatedWork W3006630499 @default.
- W4296968702 hasRelatedWork W3007420330 @default.
- W4296968702 hasRelatedWork W3093612317 @default.
- W4296968702 hasRelatedWork W4312613727 @default.
- W4296968702 hasRelatedWork W2318670660 @default.
- W4296968702 hasVolume "143" @default.
- W4296968702 isParatext "false" @default.
- W4296968702 isRetracted "false" @default.
- W4296968702 workType "article" @default.