  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.
