Influence of image noise on crack detection performance of deep convolutional neural networks
Paper
Chianese, R., Nguyen, A., Gharehbaghi, V. R., Aravinthan, T. and Noori, M.. 2021. "Influence of image noise on crack detection performance of deep convolutional neural networks." Cunha, A. and Caetano, E. (ed.) 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10). Porto, Portugal 30 Jun - 02 Jul 2021 Winnipeg, Canada.
| Paper/Presentation Title | Influence of image noise on crack detection performance of deep convolutional neural networks |
|---|---|
| Presentation Type | Paper |
| Authors | Chianese, R. (Author), Nguyen, A. (Author), Gharehbaghi, V. R. (Author), Aravinthan, T. (Author) and Noori, M. (Author) |
| Editors | Cunha, A. and Caetano, E. |
| Journal or Proceedings Title | Proceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) |
| ERA Conference ID | 43541 |
| Number of Pages | 8 |
| Year | 2021 |
| Place of Publication | Winnipeg, Canada |
| Web Address (URL) of Paper | https://arxiv.org/ftp/arxiv/papers/2111/2111.02079.pdf |
| Conference/Event | 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10) |
| International Conference on Structural Health Monitoring of Intelligent Infrastructure | |
| Event Details | International Conference on Structural Health Monitoring of Intelligent Infrastructure Rank A A A A A A A A A A A A A A A A A A A A |
| Event Details | 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Advanced Research and Real-World Applications (SHMII-10) Event Date 30 Jun 2021 to end of 02 Jul 2021 Event Location Porto, Portugal |
| Abstract | Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research reveal that noisy images have a moderate to high impact on the network's capability to accurately classify images despite the application of image pre-processing. A new index has been developed for finding the most efficient method for classification in terms of computation timing and accuracy. Consequently, AlexNet was selected as the most efficient model based on the proposed index |
| Keywords | crack detection; transfer learning; deep convolution neural network; image noise; network performance |
| ANZSRC Field of Research 2020 | 400508. Infrastructure engineering and asset management |
| Byline Affiliations | School of Civil Engineering and Surveying |
| Kharazmi University, Iran | |
| California Polytechnic State University, United States | |
| Institution of Origin | University of Southern Queensland |
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