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August 18, 2025BuildingsOpen Access

LSH-YOLO: A Lightweight Algorithm for Helmet-Wear Detection

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Authors

ZLZhao LiuFWF. WangWWWeimin Wang

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Overview

Proposed lightweight helmet detection model reduces computational cost by 63% in construction settings, indicating enhanced efficiency.

Key Points

  • LSH-YOLO increases mAP50 by 0.6%, achieving a total of 92.9% in detection accuracy.
  • The model reduces computational cost significantly, achieving a 63% decrease while lowering the parameter count by 19%.
  • The new SCDH detection head replaces YOLOv8's head, maintaining accuracy with improved efficiency.
  • Improvements enable deployment in resource-limited environments, enhancing intelligent safety surveillance.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68af431bad7bf08b1ead196bhttps://doi.org/10.3390/buildings15162918
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Lightweight Safety Helmet Detection Algorithm Based on Improved YOLO112025
  2. 2Improved YOLOv8n-based Helmet Wearing Inspection Measurement Method Study2024
  3. 3A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement2024 · 3 citations
  4. 4Helmet wearing detection algorithm based on improved YOLOv52024 · 24 citations
  5. 5HR-YOLO: A Multi-Branch Network Model for Helmet Detection Combined with High-Resolution Network and YOLOv52024 · 4 citations