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CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites

arXiv 2410.20699 ↗First posted 28 Oct 2024cs.CVcited by 0

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Abstract (arXiv)

Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 28 Oct 2024.

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9 of 19 references.

References · 9

  1. 2017Squeeze-and-Excitation Networkscited by 4,789
  2. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  3. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
  4. 2024CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 datasetcited by 24
  5. 2024Real-Time Pill Identification for the Visually Impaired Using Deep Learningcited by 21
  6. 2024AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Waycited by 15
  7. 2023Twitter Sentiment Analysis of Covid Vaccinesscited by 13
  8. 2024Research on Improved U-net Based Remote Sensing Image Segmentation Algorithmcited by 10
  9. 2024Enhanced Credit Score Prediction Using Ensemble Deep Learning Modelcited by 2

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