Stand-Alone Composite Attention Network for Concrete Structural Defect Classification

Abstract

Automation in structural health monitoring involves a critical step of automatic classification of concrete defect images/videos. Although interdisciplinary research community in AI has responded with some progress, immense challenges are still involved because of the predominant overlapping nature of the defect classes, exacerbated by the large variations in their visual appearance. However, current methodologies mostly consider single-class nonoverlapping defects and emphasize equally over the entire image plane; thus unable to focus on specific defect regions for robust feature selection. Thereby, the classification performance gets degraded and the methodologies became less suitable for real-world scenarios. In this work, we propose a novel stand-alone composite attention network that automatically exerts higher emphasis on the defective regions and less emphasis on the healthy regions to recognize multitarget multiclass and single-class concrete structural defects. This architecture stems from the novel GFGA mechanism as the building block to capture minute local features from visually-similar defect classes. We then propose the MSAM that encompasses multiscale discriminative information to capture variations in image properties. The MSAM incorporates BMAM to obtain crucial channel-spatial descriptors making the overall architecture an end-to-end-trainable network. Extensive experimentation and analysis on three large concrete defect datasets show the superiority of our proposed network as compared to the current state-of-the-art methodologies.