Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network

By Shengyuan Li, Xuefeng Zhao, Guangyi Zhou. Comput. Aided Civ. Infrastructure Eng., 2019

435 citations recorded by the source corpus.

Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes). However, most deep learning‐based methods detect structural damage at the image level and grid‐cell level. To provide pixel‐level detection of multiple damages, a Fully Convolutional Network (FCN)‐based multiple damages detection method for concrete structure is proposed. To realize this method, a database of 2,750 images (with 504 × 376 pixels) including crack, spalling, efflorescence, and hole images in concrete structure is built, and the four damages included in those images are labeled manually. Then, the architecture of the FCN is modified, trained, validated, and tested using this database. A strategy of model‐based transfer learning is used to initialize the parameters of the FCN during the training process. The results show 98.61% pixel accuracy (PA), 91.59% mean pixel accuracy (MPA), 84.53% mean intersection over union (MIoU), and 97.34% frequency weighted intersection over union (FWIoU). Subsequently, the robustness and adaptability of the trained FCN model is tested and the damage is extracted, where damage areas are provided according to a calibrated relation between the ratio (the pixel area and true area of the detected object) and the distance from the smartphone to the concrete surface using a laser range finder. A comparative study is conducted to examine the performance of the proposed FCN‐based approach using a SegNet‐based method. The results show that the proposed method substantiates quite better performance and can indeed detect multiple concrete damages at the pixel level in realistic situations.

Subfields: Civil and Structural Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/1c44a17065c0254656c6887f32bf40d2876d5996

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In Civil and Structural Engineering

Citations in the corpus

A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “684 Li et al. (2019) proposed an FCN to detect four concrete damage classes: cracks, spalling, 685 efflorescence, and holes, from an established smartphone-based image database.”
Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, methodology): “A modified FCN with fine-tuning the DenseNet-121 (a densely connected CNN) was implemented by Li et al. [25] to provide pixel-level detection of multiple damages, i.e. crack, spalling, efflorescence and holes, found on concrete surfaces.”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, methodology): “More innovative and useful ideas for monitoring tasks, for instance to detect efflorescence and spalling [56, 74]; bolts loosening [139], rutting of asphalt pavements and potholes [79], typology of cracks, their length and width [134] have been explored.”