Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance

This paper reviews recent developments in deep learning-based crack segmentation methods and investigates their performance under the impact from different image types. Publicly available datasets and commonly adopted performance evaluation metrics are also summarized. Moreover, an image dataset, namely the Fused Image dataset for convolutional neural Network based crack Detection (FIND), was released to the public for deep learning analysis. The FIND dataset consists of four different image types including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused image by combining the raw intensity and raw range image. To validate and demonstrate the performance boost through data fusion, a benchmark study is performed to compare the performance of nine (9) established convolutional neural network architectures trained and tested on the FIND dataset; furthermore, through the cross comparison, the optimal architectures and image types can be determined, offering insights to future studies and applications.