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A safety helmets and overalls detection algorithm based on improved Yolov5

Published: 15 March 2023 Publication History

Abstract

Workers in the factory need to wear safety helmets and overalls according to regulations. In order to detect whether someone violates the rules during working hours in real time, a deep learning detection algorithm based on yolov5 is proposed. The images are collected by the camera in the factory, and two different types of datasets are constructed. The experimental comparison shows that the detection accuracy and recall rate of the datasets have improved after adding additional categories. After optimizing the datasets, this paper adds coordinate attention mechanism to the backbone of yolov5 network. The results show that the improved algorithm has at least 1% improvement in recall and accuracy, and can effectively and timely detect illegal wear in complex operation scenarios.

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Han Ze-jia, Xiao Qin-kun, Zhang Li-qi. To Improve the SSD Safety Helmet Reflective Clothing Detection Method [J]. Automation and instrument, 2021, 36 (09): 63-68.
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SHI Hui, CHEN Xianqiao, YANG Ying. Safety Helmet Wearing Detection Method of Improved YOLO v3 [J]. Computer engineering and application, 2019, 55 (11): 213-220.
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Zhang gengcheng, Liang Erzhu, Wei Shaohua, Research on helmet wearing detection algorithm in blasting site based on yolov3 [J]. Internet of things technologies, 2022, 12 (04): 90-93+96.
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EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2023

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Author Tags

  1. coordinate attention
  2. detection
  3. factory
  4. overalls
  5. safety helmets
  6. yolov5

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EITCE 2022

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Overall Acceptance Rate 508 of 972 submissions, 52%

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