A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Deep Convolutional Neural Network Based Approach to Classify and Detect Crack in Concrete Surface Using Xception
Deep learning has become a widely practiced approach in research arenas related to civil infrastructures. Monitoring concrete structures is time-consuming, costly, unsafe, and laborious. Instead of manual inspection, the deep learning approach increases more possibility to automate this inspection process helping to mitigate future risk. This study introduces an automatic concrete surface crack detection and classification technique using a deep learning architecture, namely Xception to alleviate the risks due to deteriorating structure conditions. At first, the Xception model was trained and tested on a public dataset consisting of cracked and non-cracked images, and the model has shown superior accuracy in two-class classification. Afterward, the cracked sub-dataset was split into two classes–horizontally cracked and vertically cracked using a traditional computer vision approach to determine the inclination angle of a crack. The proposed deep learning model was trained on the newly formed dataset and performed remarkably in three-class classification as well. This paper demonstrates the proposed model's effectiveness, performance, and findings, providing a reference for concrete surface crack detection and classification for related domains.
A Deep Convolutional Neural Network Based Approach to Classify and Detect Crack in Concrete Surface Using Xception
Deep learning has become a widely practiced approach in research arenas related to civil infrastructures. Monitoring concrete structures is time-consuming, costly, unsafe, and laborious. Instead of manual inspection, the deep learning approach increases more possibility to automate this inspection process helping to mitigate future risk. This study introduces an automatic concrete surface crack detection and classification technique using a deep learning architecture, namely Xception to alleviate the risks due to deteriorating structure conditions. At first, the Xception model was trained and tested on a public dataset consisting of cracked and non-cracked images, and the model has shown superior accuracy in two-class classification. Afterward, the cracked sub-dataset was split into two classes–horizontally cracked and vertically cracked using a traditional computer vision approach to determine the inclination angle of a crack. The proposed deep learning model was trained on the newly formed dataset and performed remarkably in three-class classification as well. This paper demonstrates the proposed model's effectiveness, performance, and findings, providing a reference for concrete surface crack detection and classification for related domains.
A Deep Convolutional Neural Network Based Approach to Classify and Detect Crack in Concrete Surface Using Xception
Lect. Notes in Networks, Syst.
Hossain, Sazzad (editor) / Hossain, Md. Shahadat (editor) / Kaiser, M. Shamim (editor) / Majumder, Satya Prasad (editor) / Ray, Kanad (editor) / Alfaz, Nazia (author) / Hasnat, Abul (author) / Khan, Alvi Md. Ragib Nihal (author) / Sayom, Nazmus Shakib (author)
2022-10-04
15 pages
Article/Chapter (Book)
Electronic Resource
English
Autonomous concrete crack detection using deep fully convolutional neural network
| British Library Online Contents | 2019
Building Surface Crack Detections Using Deep Convolutional Neural Network (DCNN) Architectures
| Springer Verlag | 2024
Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks
| DOAJ | 2021
Automatic classification of pavement crack using deep convolutional neural network
| Taylor & Francis Verlag | 2020