• Skip to primary navigation
  • Skip to main content
Logo

Work Zone Safety Information Clearinghouse

Library of Resources to Improve Roadway Work Zone Safety for All Roadway Users

  • About
  • Join Listserv
  • Contact
  • Twitter
  • Facebook
  • LinkedIn
  • Work Zone Data
    • At a Glance
    • National & State Traffic Data
    • Work Zone Traffic Crash Trends and Statistics
    • Worker Fatalities and Injuries at Road Construction Sites
  • Topics of Interest
    • Commercial Motor Vehicle Safety
    • Smart Work Zones
    • Transportation Management Plans
    • Accommodating Pedestrians
    • Worker Safety and Welfare
    • Project Coordination in Work Zones
  • Training
    • Flagger
    • Online Courses
    • Toolboxes
    • FHWA Safety Grant Products
    • Certification and
      Accreditation
  • Work Zone Devices
  • Laws, Standards & Policies
    • COVID-19 Guidance
  • Public Awareness
  • Events
  • About
  • Listserv
  • Contact
  • Search
Publication

Automatic Detection of Hardhats Worn by Construction Personnel: A Deep Learning Approach and Benchmark Dataset

Author/Presenter: Jixiu Wu; Nian Cai; Wenjie Chen; Huiheng Wang; Guotian Wang
Abstract:

Hardhats play an essential role in protecting construction individuals from accidents. However, wearing hardhats is not strictly enforced among workers due to all kinds of reasons. To enhance construction sites safety, the majority of existing works monitor the presence and proper use of hardhats through multi-stage data processing, which come with limitations on adaption and generalizability. In this paper, a one-stage system based on convolutional neural network is proposed to automatically monitor whether construction personnel are wearing hardhats and identify the corresponding colors. To facilitate the study, this work constructs a new and publicly available hardhat wearing detection benchmark dataset, which consists of 3174 images covering various on-site conditions. Then, features from different layers with different scales are fused discriminately by the proposed reverse progressive attention to generate a new feature pyramid, which will be fed into the Single Shot Multibox Detector (SSD) to predict the final detection results. The proposed system is trained by an end-to-end scheme. The experimental results demonstrate that the proposed system is effective under all kinds of on-site conditions, which can achieve 83.89% mAP (mean average precision) with the input size 512 × 512.

Source: Automation in Construction
Volume: 106
Publication Date: October 2019
Source URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Construction Safety; Hard Hats; Video Imaging Detectors; Worker Safety

Copyright © 2023 American Road & Transportation Builders Association (ARTBA). The National Work Zone Safety Information Clearinghouse is a project of the ARTBA Transportation Development Foundation. It is operated in cooperation with the U.S. Federal Highway Administration and Texas A&M Transportation Institute. | Copyright Statement · Privacy Policy · Disclaimer
American Road and Transportation Builders Association Transportation Development Foundation, American Road and Transportation Builders Association U.S. Department of Transportation Federal Highway Administration Texas A&M Transportation Institute