Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset
This work addresses crack segmentation for infrastructure monitoring, offering a domain-specific incremental improvement with a new dataset.
The paper tackles high-precision pixel-level crack segmentation in infrastructure by introducing boundary features into a model called BGCrack, achieving improved segmentation results as verified experimentally. It also provides a new steel crack dataset to address the lack of standard benchmarks for steel structures in civil infrastructure.
Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern. Unlike the common research paradigms that adopt novel artificial intelligence (AI) methods directly, this paper examines the inherent characteristics of cracks so as to introduce boundary features into crack identification and then builds a boundary guidance crack segmentation model (BGCrack) with targeted structures and modules, including a high frequency module, global information modeling module, joint optimization module, etc. Extensive experimental results verify the feasibility of the proposed designs and the effectiveness of the edge information in improving segmentation results. In addition, considering that notable open-source datasets mainly consist of asphalt pavement cracks because of ease of access, there is no standard and widely recognized dataset yet for steel structures, one of the primary structural forms in civil infrastructure. This paper provides a steel crack dataset that establishes a unified and fair benchmark for the identification of steel cracks.