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.

翻译：裂缝是基础设施性能退化的关键指标，实现高精度像素级裂缝分割是备受关注的问题。不同于直接采用新型人工智能（AI）方法的常见研究范式，本文深入探究裂缝的固有特性，将边界特征引入裂缝识别过程，并构建了具有针对性结构与模块的边界引导裂缝分割模型（BGCrack），包括高频模块、全局信息建模模块、联合优化模块等。大量实验验证了所提设计的可行性以及边缘信息在提升分割效果方面的有效性。此外，鉴于现有知名开源数据集主要因采集便利而集中于沥青路面裂缝，而钢结构作为土木基础设施的主要结构形式之一，目前仍缺乏标准且广泛认可的该领域数据集。本文提供了一套钢裂缝数据集，为钢裂缝识别建立了统一且公平的基准。