4.7 Article

Attention-based generative adversarial network with internal damage segmentation using thermography

Journal

AUTOMATION IN CONSTRUCTION
Volume 141, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.autcon.2022.104412

Keywords

Internal damage detection; Deep learning; Light-weight segmentation; Concrete damages; Pixel-level; Infrared thermography; Real-time processing; Computer vision; GAN; Attention

Funding

  1. Canada Foundation for Innovation Grant (CFI JELF Grant) [37394]

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This paper introduces a real-time, high-performance deep-learning network for pixel-level segmentation of internal damages in concrete members using active thermography. By developing an attention-based generative adversarial network (AGAN) to train the proposed internal damage segmentation network (IDSNet), it achieved excellent performance on the test set.
This paper describes a real-time, high-performance deep-learning network to segment internal damages of concrete members at the pixel level using active thermography. Unlike surface damage, the collection and preparation of ground truth data for internal damage is extremely challenging and time consuming. To overcome these critical limitations, an attention-based generative adversarial network (AGAN) was developed to generate synthetic images for training the proposed internal damage segmentation network (IDSNet). The developed IDSNet outperforms other state-of-the-art networks, with a mean intersection over union of 0.900, positive predictive value of 0.952, F1-score of 0.941, and sensitivity of 0.942 over a test set. AGAN improves 12% of the mIoU of the IDSNet. IDSNet can perform real-time processing of 640 x 480 x 3 sizes of thermal images with 74 frames per second due to its extremely lightweight segmentation network with only 0.085 M total learnable parameters.

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