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