Data-Efficient Object Detection on Construction Sites Using Reweighting Mechanism and Cross-Batch Contrastive Learning

Deep learning-based object detectors face unique challenges in construction sites, including severe occlusions from complex spatial arrangements, similar appearances between different construction elements, and computational constraints of edge devices. While few-shot detection methods reduce annotation requirements, existing approaches using computationally intensive architectures [e.g., Faster region-based convolutional neural network (R-CNN) or detection transformer (DETR)] struggle to address these construction-specific challenges while maintaining real-time performance. To tackle these issues, we propose a lightweight few-shot detection framework based on CenterNet2, specifically designed for construction environments. Our framework introduces, first a dual reweighting mechanism to handle severe occlusions and complex spatial relationships, second a cross-batch contrastive learning strategy to enhance feature discrimination between similar construction objects, and third an efficient architecture suitable for edge deployment. Experimental results demonstrate our framework achieves 56.6% mean average precision while maintaining real-time performance, significantly outperforming existing methods in construction-specific scenarios.