Transfer learned deep feature based crack detection using support vector machine: a comparative study.

Autor: Bhalaji Kharthik KS; Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, 641112, India., Onyema EM; Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria. mikedreamcometrue@gmail.com.; Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. mikedreamcometrue@gmail.com., Mallik S; Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA. sauravmtech2@gmail.com., Siva Prasad BVV; School of Engineering (CSE), Anurag University, Hyderabad, India., Qin H; Department of Computer Science and Engineering, The University of Tennessee at Chattanooga, Chattanooga, TN, USA. hong-qin@utc.edu., Selvi C; Department of Computer Science and Engineering, Indian Institute of Information Technology, Kottayam, Kerala, 686635, India., Sikha OK; Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, 641112, India.; Dept. of Information and Communication Technologies, BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jun 24; Vol. 14 (1), pp. 14517. Date of Electronic Publication: 2024 Jun 24.
DOI: 10.1038/s41598-024-63767-5
Abstrakt: Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje