Automatic detection and recognition of safety helmet wearing based on video analysis is
important to ensure production safety. It is inefficient to supervise whether workers wear …

The vigorous development of the construction industry has also brought unprecedented
safety risks. The wearing of safety helmets at the construction site can effectively reduce …

Wearing safety helmets can effectively protect workers safety on construction sites. However,
workers often take off the helmets because of weak security-conscious and discomfort, then …

Wearing safety helmets can effectively protect workers safety on construction sites. However,
workers often take off the helmets because of weak security-conscious and discomfort, then …

Wearing a safety helmet is an essential aspect of safety production management. To
address the issue of low detection accuracy of existing safety helmet detection algorithms for …

Wearing a safety helmet is important in construction and manufacturing industrial activities to
avoid unpleasant situations. This safety compliance can be ensured by developing an …

YOLOv5 (DCMS-YOLOv5), an improvement of YOLOv5, to increase the recognition
accuracy of helmet detection methods. The model has a dual-channel architecture, and …

In order to reduce the probability of safety accidents caused by non-wearing helmet, we
proposed an improved Safety helmet wearing detection method based on Faster R-CNN …

Automatic helmet-wearing detection is an effective way to prevent head injuries for
construction site workers. However, the current helmet detection algorithms still need to …

In response to the current challenges of low detection accuracy in traditional safety helmet
detection network models, this paper introduces a novel model called …