Construction project is sensitive to material arrival delays, which can cause schedule delays and budget overruns. The prompt detection of construction material arrival delays is necessary to recover disrupted projects in time. This paper explores computer vision-based (CVB) mutually coupled detection and tracking of transport trucks for monitoring construction material arrival delays. Through the mutually coupled mechanism, the truck detection and tracking algorithms complement each other in reducing accumulated tracking errors, modifying false positives and false negatives in detection, and stabilizing the detected bounding boxes. The experimental results indicate integrating a deep Convolutional Neural Network (CNN), a Kanade-Lucas-Tomasi (KLT) corner feature tracker, and a hash-based occlusion handling strategy can achieve high tracking precision compared to state-of-the-art trackers in construction. The field application results indicate that the proposed method can achieve the automatic monitoring of construction material arrival delays.

建设项目对材料到达延迟很敏感，这可能导致进度延迟和预算超支。及时发现建筑材料到达延误对于及时恢复中断的项目是必要的。本文探讨了基于计算机视觉 (CVB) 的运输卡车的相互耦合检测和跟踪，用于监控建筑材料到达延迟。通过相互耦合的机制，卡车检测和跟踪算法在减少累积跟踪误差、修正检测中的误报和漏报以及稳定检测到的边界框方面​​相互补充。实验结果表明集成了深度卷积神经网络 (CNN)、Kanade-Lucas-Tomasi (KLT) 角点特征跟踪器，与建筑中最先进的跟踪器相比，基于散列的遮挡处理策略可以实现更高的跟踪精度。现场应用结果表明，该方法可以实现建筑材料到货延误的自动监测。