An automated crack detection method for underwater structures based on multilevel DWT and LPQ feature generation
Author:
Funder
Firat University Scientific Research Projects Management Unit
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15229-9.pdf
Reference42 articles.
1. Agnes S (2020) A Study of Methods using Image Processing Technique in Crack Detection. In: Proceedings of the Second International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2020). pp 578–582
2. Akbal E, Tuncer T (2021) FusedTSNet: An automated nocturnal sleep sound classification method based on a fused textural and statistical feature generation network. Appl Acoust 171:107559. https://doi.org/10.1016/j.apacoust.2020.107559
3. Baygin M, Yaman O, Tuncer T et al (2021) Biomedical Signal Processing and Control Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 70:102936. https://doi.org/10.1016/j.bspc.2021.102936
4. Chen FC, Jahanshahi MR (2018) NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion. IEEE Trans Ind Electron 65:4392–4400. https://doi.org/10.1109/TIE.2017.2764844
5. Chen CP, Wang J, Zou L et al (2012) A novel crack detection algorithm of underwater dam image. 2012 Int Conf Syst Informatics, ICSAI 2012 1825–1828.https://doi.org/10.1109/ICSAI.2012.6223399
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