Abstract
The construction industry faces significant challenges in optimizing equipment utilization, as underused machinery leads to increased operational costs and project delays. Accurate and timely monitoring of equipment activity is therefore key to identifying idle periods and improving overall efficiency. This paper presents the Edge-IMI framework for detecting idle construction machinery, specifically designed for integration with surveillance camera systems. The proposed solution consists of three components: object detection, tracking, and idle state identification, which are tailored for execution on resource-constrained, CPU-based edge computing devices. The performance of Edge-IMI is evaluated using a combined dataset derived from the ACID and MOCS benchmarks. Experimental results confirm that the object detector achieves an F1 score of 71.75%, indicating robust real-world detection capabilities. The logistic regression-based idle identification module reliably distinguishes between active and idle machinery with minimal false positives. Integrating all three modules, Edge-IMI enables efficient on-site inference, reducing reliance on high-bandwidth cloud services and costly hardware accelerators. We also evaluate the performance of object detection models on Raspberry Pi 5 and an Intel NUC platforms, as example edge computing platforms. We assess the feasibility of real-time processing and the impact of model optimization techniques.
Abstract (translated)
建筑行业在优化设备利用率方面面临重大挑战，因为未充分利用的机械设备会导致运营成本增加和项目延期。因此，准确且及时地监测设备活动是识别闲置时间并提高整体效率的关键。本文提出了Edge-IMI框架，专门用于检测闲置的施工机械，并设计为与监控摄像头系统集成。所提出的解决方案由三个组成部分构成：物体检测、跟踪和闲置状态识别，这些部分特别针对资源受限的CPU基边缘计算设备进行优化。 使用来自ACID和MOCS基准测试组合的数据集对Edge-IMI性能进行了评估。实验结果显示，物体检测器实现了71.75%的F1分数，表明其具有强大的实际环境检测能力。基于逻辑回归的闲置状态识别模块能够可靠地区分活动设备和闲置设备，并且误报率极低。通过集成所有三个模块，Edge-IMI能够在现场实现高效的推理，减少对高带宽云服务和昂贵硬件加速器的依赖。 我们还评估了在Raspberry Pi 5和Intel NUC平台（作为边缘计算示例）上物体检测模型的性能，并研究实时处理的可能性以及模型优化技术的影响。
URL
https://arxiv.org/abs/2506.00904
