ArticleScientific reports2024
An improved YOLOv8 safety helmet wearing detection network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A method for detecting safety helmets underground based on the YOLOv11-SRA model.Scientific reports · 2026Article
- Domain-adaptive faster R-CNN for non-PPE identification on construction sites from body-worn and general images.Scientific reports · 2026Article
- License plate recognition methodology in complex scenarios based on CSCM-YOLOv8 and CSM-LPRNet.PloS one · 2026Article
- Enhanced YOLOv8n-Based Three-Module Lightweight Helmet Detection System.Sensors (Basel, Switzerland) · 2025Article
- DAF-YOLO: Detection of Unsafe Behaviors on Construction Sites.Sensors (Basel, Switzerland) · 2025Article
- Evaluation of Helmet Wearing Compliance: A Bionic Spidersense System-Based Method for Helmet Chinstrap Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility.Sensors (Basel, Switzerland) · 2025Article
- High-Precision and Lightweight Model for Rapid Safety Helmet Detection.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In the field of industrial safety, wearing helmets plays a vital role in ensuring workers' health. Aiming at addressing the complex background in the industrial environment, caused by differences in distance, the helmet small target wearing detection methods for misdetection and omission detection problems are needed. An improved YOLOv8 safety helmet wearing detection network is proposed to enhance the capture of details, improve multiscale feature processing and improve the accuracy of small target detection by introducing Dilation-wise residual attention module, atrous spatial pyramid pooling and normalized Wasserstein distance loss function. Experiments were conducted on the SHWD dataset, and the results showed that the mAP of the improved network improved to 92.0%, which exceeded that of the traditional target detection network in terms of accuracy, recall, and other key metrics. These findings further improved the detection of helmet wearing in complex environments and greatly enhanced the accuracy of detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.