Abstract Measuring the concrete slump is necessary for ensuring the quality of concrete before its use. However, manual slump measurements have limited accuracy and are time-consuming and labor-intensive. Herein, an approach based on deep learning and stereo vision techniques is proposed to effectively improve the determination of concrete slump in outdoor environments. Input images obtained from a stereo camera system were classified into three slump cases via deep learning. The actual slumps were then calculated using depth maps obtained via stereo vision. The results were analyzed for the correlations between camera mounting heights and the distance between the cameras to identify the optimal settings for an onsite working system. A baseline of 70 mm and working height of 1.7–1.9 m yielded optimal results with errors ≤2.05%. Additionally, the mask-region-based volumetric results exhibited errors of

中文翻译：

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结合深度学习和立体视觉的原位混凝土坍落度测试

摘要 混凝土坍落度的测量是保证混凝土在使用前的质量所必需的。然而，手动坍落度测量的准确性有限，并且费时费力。在此，提出了一种基于深度学习和立体视觉技术的方法，以有效改善室外环境中混凝土坍落度的确定。从立体相机系统获得的输入图像通过深度学习分为三种坍塌情况。然后使用通过立体视觉获得的深度图计算实际坍落度。分析结果以了解摄像机安装高度与摄像机之间的距离之间的相关性，以确定现场工作系统的最佳设置。70 mm 的基线和 1.7-1.9 m 的工作高度产生了最佳结果，误差≤2.05%。此外，