  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.
