Automating visual inspection for capturing defects based on civil structures appearance is crucial due to its currently labour-intensive and time-consuming nature. An important aspect of automated inspection is image acquisition, which is rapid and cost-effective considering the pervasive developments in both software and hardware computing in recent years. Previous studies largely focused on concrete and asphalt, with less attention to masonry cracks. The latter also lacks publicly available datasets. In this paper, we first present a corresponding data set for instance segmentation with 1,300 annotated images (640 pixels x 640 pixels), named as MCrack1300, covering bricks, broken bricks, and cracks. We then test several leading algorithms for benchmarking, including the latest large-scale model, the prompt-based Segment Anything Model (SAM). We fine-tune the encoder using Low-Rank Adaptation (LoRA) and proposed two novel methods for automation of SAM execution. The first method involves abandoning the prompt encoder and connecting the SAM encoder to other decoders, while the second method introduces a learnable self-generating prompter. In order to ensure the seamless integration of the two proposed methods with SAM encoder section, we redesign the feature extractor. Both proposed methods exceed state-of-the-art performance, surpassing the best benchmark by approximately 3% for all classes and around 6% for cracks specifically. Based on successful detection, we propose a method based on a monocular camera and the Hough Line Transform to automatically transform images into orthographic projection maps. By incorporating known real sizes of brick units, we accurately estimate crack dimensions, with the results differing by less than 10% from those obtained by laser scanning. Overall, we address important research gaps in automated masonry crack detection and size estimation.

翻译：自动化的视觉检测对于基于结构外观的缺陷捕捉至关重要，因其当前的人工检测方式劳动强度大且耗时长。自动化检测的一个重要方面是图像采集，考虑到近年来软硬件计算能力的飞速发展，这一过程快速且成本低廉。以往研究主要聚焦于混凝土和沥青，对砌体裂缝的关注较少，后者也缺乏公开可用的数据集。本文首先针对实例分割任务构建了一个包含1300张标注图像（640像素×640像素）的专用数据集，命名为MCrack1300，涵盖了砖块、破损砖块和裂缝三种类别。随后，我们测试了多个领先的基准算法，包括最新的大模型——基于提示的Segment Anything Model（SAM）。我们采用低秩自适应（LoRA）对编码器进行微调，并提出了两种实现SAM自动化的新方法。第一种方法摒弃了提示编码器，将SAM编码器与其他解码器直接连接；第二种方法引入了一个可学习的自生成提示器。为确保两种方法能与SAM编码器部分无缝集成，我们重新设计了特征提取器。两种方法均超越了现有最优性能，所有类别的平均准确率比最佳基准高约3%，裂缝类别的准确率提升约6%。基于成功检测的结果，我们提出了一种基于单目相机和霍夫线变换的方法，可自动将图像转换为正射投影图。通过引入已知的砖块实际尺寸，我们准确估计了裂缝尺寸，其结果与激光扫描结果差异小于10%。总体而言，本文填补了砌体裂缝自动化检测与尺寸估计的重要研究空白。