Safety helmet detection based on face detection and regression

Yating Huang; Lingrui Zhu

doi:10.1117/12.2671555

16 March 2023 Safety helmet detection based on face detection and regression

Yating Huang, Lingrui Zhu

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259318 (2023) https://doi.org/10.1117/12.2671555
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

Wearing a safety helmet can effectively reduce or prevent injury to the worker's head caused by hazardous materials in the construction site. However, due to poor supervision, safety accidents often occur when workers don't wear safety helmets. In this paper, we propose a safety helmet detection algorithm based on face detection and ridge regression. Firstly, we get the location information of the face box and the five key points of the face through face detection algorithm, and then get the helmet detection box corresponding to face through ridge regression model. We collected 4000 images of people wearing helmets for training and testing of ridge regression models. Compared with some of the most advanced methods, we have achieved very good results in the test set. The results show that mIoU reaches 70.118% and the detection rate is improved.

Citation Download Citation

Yating Huang and Lingrui Zhu "Safety helmet detection based on face detection and regression", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259318 (16 March 2023); https://doi.org/10.1117/12.2671555

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available