Personal Protective Equipment Detection in Extreme Construction Conditions
This addresses safety management in construction by enhancing detection robustness under extreme conditions, but it is incremental as it builds on existing YOLOv5 with neural style transfer.
The paper tackled the problem of personal protective equipment detection performance declining in extreme construction conditions by developing NST-YOLOv5, which improved mAP by 0.141 and 0.083 on synthesized and real-world data.
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.