@inproceedings{6f3150f95ce849e8aa1a16ee75f05d29,
title = "Safety Helmet Wearing Detection Based on Image Processing and Deep Learning",
abstract = "The environment of the steel factory workshop is complex, and there may be a variety of unexpected potential dangers, so wearing a helmet to enter the workshop is a prerequisite for the factory. In order to supervise this situation, it is necessary for employees to wear helmets for testing, which is a key part of the overall intelligent monitoring system for steel plant personnel. In this paper, through the crawler to collect high-definition employees wearing helmets and no helmet pictures, using manual labeling, proposed a helmet detection framework based on computer vision deep learning detection framework Faster-RCNN. The actual testing results produce convincing experimental results, which proves the effectiveness and practicability of the proposed framework.",
keywords = "Faster-RCNN, deep learning, helmet detection, image processing",
author = "Wei Zhang and Yang, \{Chi Fu\} and Feng Jiang and Gao, \{Xian Zhong\} and Xiao Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020 ; Conference date: 03-07-2020 Through 05-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CISCE50729.2020.00076",
language = "英语",
series = "Proceedings - 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "343--347",
booktitle = "Proceedings - 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020",
address = "美国",
}