An Edge-Computing-Driven Approach for Augmented Detection of Construction Materials: An Example of Scaffold Component Counting

Construction material management is crucial for project progression. Counting massive amounts of scaffold components is a key step for efficient material management. However, traditional counting methods are time-consuming and laborious. Utilizing a vision-based method with edge devices for counting these materials undoubtedly offers a promising solution. This study proposed an edge-computing-driven approach for detecting and counting scaffold components. Two algorithm refinements of YOLOX, including generalized intersection over union (GIoU) and soft non-maximum suppression (Soft-NMS), were introduced to enhance detection accuracy in conditions of occlusion. An automated pruning method was proposed to compress the model, achieving a 60.2% reduction in computation and a 9.1% increase in inference speed. Two practical case studies demonstrated that the method, when deployed on edge devices, achieved 98.9% accuracy and reduced time consumption for counting tasks by 87.9% compared to the conventional method. This research provides an edge-computing-driven framework for counting massive materials, establishing a comprehensive workflow for intelligent applications in construction management. The paper concludes with limitations of the current study and suggestions for future work.