Object detection has been widely applied for construction safety management,
especially personal protective equipment (PPE) detection. Though the existing
PPE detection models trained on conventional datasets have achieved excellent
results, their performance dramatically declines in extreme construction
conditions. A robust detection model NST-YOLOv5 is developed by combining the
neural style transfer (NST) and YOLOv5 technologies. Five extreme conditions
are considered and simulated via the NST module to endow the detection model
with excellent robustness, including low light, intense light, sand dust, fog,
and rain. Experiments show that the NST has great potential as a tool for
extreme data synthesis since it is better at simulating extreme conditions than
other traditional image processing algorithms and helps the NST-YOLOv5 achieve
0.141 and 0.083 mAP_(05:95) improvements in synthesized and real-world extreme
data. This study provides a new feasible way to obtain a more robust detection
model for extreme construction conditions.

通过结合神经风格迁移（NST）和 YOLOv5 技术，开发了一种稳健的检测模型 NST-YOLOv5，用于在极端建筑条件下进行个人防护装备（PPE）检测，并通过模拟灯光不足、强光、沙尘、雾和雨等五种极端条件赋予该模型极佳的鲁棒性。实验结果表明 NST 在合成极端数据方面具有较高潜力，优于其他传统图像处理算法，并帮助 NST-YOLOv5 在合成和真实极端数据上分别提升了 0.141 和 0.083 mAP_(05:95)，为在极端建筑条件下获取更加稳健的检测模型提供了新的可行方式。