Personal Protective Equipment Detection in Extreme Construction Conditions

TL;DR

This paper introduces NST-YOLOv5, a robust PPE detection model for extreme construction conditions, using neural style transfer to simulate challenging environments and improve detection accuracy.

Contribution

The study develops a novel PPE detection model combining neural style transfer with YOLOv5, enhancing robustness in extreme construction scenarios.

Findings

Achieves significant mAP improvements in extreme conditions

Neural style transfer effectively simulates challenging environments

Outperforms traditional image processing methods in data synthesis

Abstract

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.