MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

It is an essential measure for workers to wear safety helmets when entering the construction site to prevent head injuries caused by object collision and falling. This paper proposes a lightweight algorithm for helmet-wearing detection based on YOLOV5, which is faster and more robust for helmet dete...

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Bibliographic Details
Main Authors: Cheng Sun, Shiwen Zhang, Peiqi Qu, Xingjin Wu, Peng Feng, Zhanya Tao, Jin Zhang, Ying Wang
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Applied Sciences
Subjects:
helmet-wearing detection
YOLOv5
attention mechanism
model pruning
Online Access:https://www.mdpi.com/2076-3417/12/19/9697

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