Irregular Openings Identification at Construction Sites Based on Few-Shot Learning

The construction industry frequently encounters safety hazards, with falls related to undetected openings being a major cause of fatalities. Identifying unstructured openings using computer vision is challenging due to their unpredictable nature and the difficulty of acquiring large labeled datasets in dynamic construction environments. Conventional deep learning methods require substantial data, limiting their applicability. Few-shot learning (FSL) offers a promising alternative by enabling models to learn from limited examples. This study investigates the effectiveness of an FSL approach, specifically model-agnostic meta-learning (MAML), enhanced with domain-specific attributes, for identifying unstructured openings with minimal labeled data. We developed and evaluated an attribute-enhanced MAML framework under various few-shot conditions (k-way, n-shot) and compared its performance against conventional supervised fi-ne-tuning. The results demonstrate that the proposed FSL model achieved high classification accuracy (over 90.5%) and recall (over 85.5%) using only five support shots per class. Notably, the FSL approach significantly outperformed supervised fine-tuning methods under the same limited data conditions, exhibiting substantially higher recall crucial for safety monitoring. These findings validate that FSL, augmented with relevant attributes, provides a data-efficient and effective solution for monitoring unpredictable hazards like unstructured openings, reducing the reliance on extensive data annotation. This research contributes valuable insights for developing adaptive and robust AI-powered safety monitoring systems in the construction domain.