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

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 x 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.