用视频分析自动预测混凝土坍落度，无需传感器和人工干预。
SlumpGuard: An AI-Powered Real-Time System for Automated Concrete Slump Prediction via Video Analysis
- 单个固定摄像头捕捉搅拌车出料过程，自动识别浇筑事件
- 在6000+视频片段上实现高精度坍落度分类与浇筑检测
- 解决人工估算不一致问题，适合工地实时质量监控
混凝土工作性对施工质量至关重要，坍落度测试是现场最常用的评估方法。但传统测试依赖人工、耗时且结果受操作者影响大，难以实现实时或连续监测。为此，我们提出SlumpGuard，一种基于AI的视觉系统，通过单个固定摄像头分析搅拌车卸料口的自然出料流态，自动完成卸料口定位、浇筑事件识别与视频驱动的坍落度分类，实现无传感器、免安装、无需人工介入的质量监控。本文介绍系统设计，构建了超过6000段复现工地场景的视频数据集，并通过大量实验验证了其在复杂现场条件下可靠的卸料口定位、精准的浇筑事件检测及鲁棒的坍落度预测能力。专家研究显示人工视觉估计存在显著差异，凸显自动化评估的必要性。
原文摘要 · Abstract (English)
Concrete workability is essential for construction quality, with the slump test being the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, making it unsuitable for continuous or real-time monitoring during placement. To address these limitations, we present SlumpGuard, an AI-powered vision system that analyzes the natural discharge flow from a mixer-truck chute using a single fixed camera. The system performs automatic chute detection, pouring-event identification, and video-based slump classification, enabling quality monitoring without sensors, hardware installation, or manual intervention. We introduce the system design, construct a site-replicated dataset of over 6,000 video clips, and report extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals significant disagreement in human visual estimates, highlighting the need for automated assessment.
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