Gaurab Bhattacharya
Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification
Bhattacharya, Gaurab; Mandal, Bappaditya; Puhan, Niladri B.
Abstract
In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defects in concrete structures found in civil infrastructures. The MDAL network is composed of interleaved MSCA and FGIA modules to encode crucial fine-grained deformation-aware information from concrete images. The novel attention mechanism is able to localize specific defect regions within an image and extracts crucial discriminative information in multi-scale fashion ranging from coarser to finer features without using any preprocessing step, such as region-of-interest selection or denoising. Our proposed attention mechanism enables the MDAL architecture to automatically classify multiple overlapping defect classes present in the concrete images and leads to an end-to-end trainable deep network. Experimental results on three large concrete defect datasets and ablation studies show that our MDAL network outperforms the current state-of-the-art methodologies significantly.
Citation
Bhattacharya, G., Mandal, B., & Puhan, N. B. (2020). Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3707-3713. https://doi.org/10.1109/TCSVT.2020.3028008
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 30, 2020 |
Online Publication Date | Sep 30, 2020 |
Publication Date | Sep 30, 2020 |
Deposit Date | Jun 5, 2023 |
Journal | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
Print ISSN | 1051-8215 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 31 |
Issue | 9 |
Pages | 3707-3713 |
DOI | https://doi.org/10.1109/TCSVT.2020.3028008 |
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