ArticleSensors (Basel, Switzerland)2023
Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Research on super-resolution reconstruction of construction images based on attention mechanism and generative adversarial networks.Scientific reports · 2026Article
- Safety helmet detection methods in heavy machinery factory.Scientific reports · 2025Article
- Article
- Research on helmet wearing detection method based on deep learning.Scientific reports · 2024Article
- Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards.Sensors (Basel, Switzerland) · 2023Article
- PNANet: Probabilistic Two-Stage Detector Using Pyramid Non-Local Attention.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries.
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Registered trials
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