Towards Edge-Based Idle State Detection in Construction Machinery Using Surveillance Cameras

TL;DR

This paper introduces Edge-IMI, a framework for detecting idle construction machinery using surveillance cameras on resource-limited edge devices, improving efficiency and reducing reliance on cloud computing.

Contribution

The paper presents a novel edge-based framework for real-time idle detection in construction machinery, optimized for CPU devices, with validated performance on real-world datasets.

Findings

Object detector achieves 71.75% F1 score.

Reliable idle state classification with minimal false positives.

Feasibility demonstrated on Raspberry Pi 5 and Intel NUC.

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.