This paper presents a method for automatic detection and quantification of full cross-sectional dimensions of structural components using oblique photography and the SAM-dimension (Segment Anything Model-dimension) model. Unlike traditional methods that measure only a single cross-section, this approach enables full cross-sectional dimension detection across the entire component, enhancing efficiency and coverage. The method utilizes the camera's crosshair position within the component area to adjust the model's prompt module strategy, allowing operation without human intervention. A binary mask is generated by fusing the model's output with the original image. Additionally, the method incorporates local unit methods, connected domain analysis, and mask feature extraction for dimension quantification. Experimental results show the model's adaptability to varying distances and lighting, accurately segmenting square, circular, and variable cross-section components. The relative errors for column components' cross-sectional width and height are within 1.22 %, and for beam-type components, within 3.02 %, demonstrating robust and generalized performance.

中文翻译：

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使用基于 Segment Any 模型 （SAM） 的分割自动检测和量化结构组件尺寸

本文提出了一种使用倾斜摄影和 SAM 维度 （Segment Anything Model-dimension） 模型自动检测和量化结构构件完整横截面尺寸的方法。与仅测量单个横截面的传统方法不同，这种方法可以对整个组件进行完整的横截面尺寸检测，从而提高效率和覆盖率。该方法利用摄像头在组件区域内的准线位置来调整模型的提示模块策略，从而允许在无需人工干预的情况下进行作。通过将模型的输出与原始图像融合来生成二进制掩码。此外，该方法还结合了局部单元方法、连接域分析和掩码特征提取以进行维度量化。实验结果表明，该模型对不同距离和照明的适应性，可以准确分割方形、圆形和可变横截面分量。柱组件的横截面宽度和高度的相对误差在 1.22 % 以内，梁型组件的相对误差在 3.02 % 以内，表明了稳健和通用的性能。