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An Integrated CNN-RNN Framework to Assess Road Crack
Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
University of Chittagong, Bangladesh.ORCID-id: 0000-0002-7473-8185
Chittagong University of Engineering & Technology.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. (Pervasive Mobile Computing)ORCID-id: 0000-0003-0244-3561
2019 (engelsk)Inngår i: Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT), 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Road crack detection and road damage assessment are necessary to support driving safety in a route network. Several unexpected incidents (e.g. road accidents) take place all over the world due to unhealthy road infrastructure. This paper proposes a deep learning approach for road crack detection and road damage assessment which will contribute to the transport sector of a country like Bangladesh where a plethora of roads undergo the crack problem. The proposed model consists of two phases. In the first phase, the model is trained using transfer learning (VGG16) to detect the existence of crack on the road surface. In the second phase, an integrated framework, combining CNN (VGG16) and RNN (LSTM), is trained to classify the crack in one of the two categories-severe and slight. After experiments, the validation accuracies obtained by the proposed models (VGG16 and VGG16-LSTM) are respectively 99.67% and 97.66%.

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
Vgg16, Integrated framework, Validation accuracy, Road crack detection, Damage assessment
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-76655OAI: oai:DiVA.org:ltu-76655DiVA, id: diva2:1368930
Konferanse
2019 22nd International Conference on Computer and Information Technology (ICCIT)
Prosjekter
A belief-rule-based DSS to assess flood risks by using wireless sensor networksTilgjengelig fra: 2019-11-08 Laget: 2019-11-08 Sist oppdatert: 2019-11-15

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