Can Generative AI-Generated Images Effectively Support and Enhance Real-World Construction Helmet Detection?

Although computer vision methods have advanced in construction helmet detection in recent years, their performance heavily depends on large-scale, class-balanced, and diverse annotated datasets. To address the high cost and labor-intensive nature of traditional data collection and annotation, this study introduces a novel helmet detection dataset named AIGC-HWD (Artificial Intelligence-Generated Content–Helmet Wearing Detection), automatically generated using generative AI tools. The dataset contains five categories of labels, supporting both helmet-wearing detection and color classification tasks. We evaluate the standalone performance of AIGC-HWD, as well as its augmentation effect when combined with the real-world dataset GDUT-HWD, using multiple algorithms, including YOLO v8, YOLO v10, YOLO 11, YOLO v11-MobileNet v4, YOLO v13, Faster R-CNN, and RT-DETR. Experimental results show that models trained solely on AIGC-generated images can achieve mAP@50 scores exceeding 0.7 and 0.8 on real-world images in two separate tests, demonstrating a certain level of generalization. When used for data augmentation alongside real-world images, the performance improves to varying degrees—by approximately 1% on the YOLO series, and by over 10% on the two-stage algorithm Faster R-CNN. These findings validate the potential of generative AI images for safety monitoring in construction scenarios and provide new insights into the integration of synthetic and real-world data.