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。

极端施工条件下的个人防护装备检测

Personal Protective Equipment Detection in Extreme Construction  Conditions

Detecting Personal Protective Equipment in images and video streams is a
relevant problem in ensuring the safety of construction workers. In this
contribution, an architecture enabling live image recognition of such equipment
is proposed. The solution is deployable in two settings -- edge-cloud and
edge-only. The system was tested on an active construction site, as a part of a
larger scenario, within the scope of the ASSIST-IoT H2020 project. To determine
the feasibility of the edge-only variant, a model for counting people wearing
safety helmets was developed using the YOLOX method. It was found that an
edge-only deployment is possible for this use case, given the hardware
infrastructure available on site. In the preliminary evaluation, several
important observations were made, that are crucial to the further development
and deployment of the system. Future work will include an in-depth
investigation of performance aspects of the two architecture variants.

本文提出了一种在线识别人们佩戴个人防护装备的架构，可用于确保建筑工人的安全，可以在边缘 - 云和仅边缘的两种设置下部署，使用 YOLOX 方法在建筑工地上进行了测试，初步评估表明，对于这种用例，边缘部署是可行的，未来的工作将包括对两种架构变体性能方面进行深入研究和部署。