Improvement of Construction Workers’ Drowsiness Detection and Classification via Text-to-Image Augmentation and Computer Vision

Detecting and classifying construction workers’ drowsiness is critical in the construction safety management domain. Research efforts to increase the reliability of drowsiness detection through image augmentation and computer vision approaches face two key challenges: the related size constraints and the number of manual tasks associated with creating input images necessary for training vision algorithms. Although text-to-image (T2I) has emerged as a promising alternative, the dynamic relationship between T2I-driven image characteristics (e.g., contextual relevance), different computer vision algorithms, and the resulting performance remains lacking. To address the gap, this study proposes T2I-centered computer vision approaches for enhanced drowsiness detection by creating four separate image sets (e.g., construction vs. non-construction) labeled using the polygon method, developing two detection models (YOLOv8 and YOLO11), and comparing the performance. The results showed that the use of construction domain-specific images for training both YOLOv8 and YOLO11 led to higher mAP@50 of 68.2% and 56.6%, respectively, compared to those trained using non-construction images (53.4% and 53.5%). Also, increasing the number of T2I-generated training images improved mAP@50 from 68.2% (baseline) to 95.3% for YOLOv8 and 56.6% to 93.3% for YOLO11. The findings demonstrate the effectiveness of leveraging the T2I augmentation approach for improved construction workers’ drowsiness detection.