Bi-Model Helmet Wearing Detection

DOI

10.2991/978-2-494069-31-2_229How to use a DOI?

Keywords

CV; Pifpaf; Yolov5; data combination

Abstract

The conflict between workers’ personal will and the construction side’s safety needs has existed for many years, until the idea of using algorithms to detect if workers are all wearing helmets came to fruition. This paper will use the combination of two mainstream computer visualization tools, Pifpaf and Yolo, to detect if the helmet is on the worker’s head. Benefit from the existing powerful recognition tools, all the workers showing on the same screen can be detected altogether, which largely increases the efficiency. After combining two detection models, the precision is 13% higher than just using a single model, and the correctness rate of using the combination of yolo and pifpaf is more than 90%, with the processing speed unchanged.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yongtai Yang
PY  - 2022
DA  - 2022/12/29
TI  - Bi-Model Helmet Wearing Detection
BT  - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
PB  - Atlantis Press
SP  - 1947
EP  - 1953
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-31-2_229
DO  - 10.2991/978-2-494069-31-2_229
ID  - Yang2022
ER  -