Author:
Cao Ruiyun,Li Hongxiang,Yang Banglie,Feng Ao,Yang Jie,Mu Jiong
Abstract
Abstract
Wearing safety helmet correctly is an effective means to reduce the accident rate and ensure the safety of construction personnel. But in the complex work site, the helmet detection task will face a variety of challenges. This paper uses YOLOV4’s deep neural network architecture and makes fine-tuning and improvement according to the characteristics and difficulties of this task, proposes a new helmet detection system, and achieves 95.1% accuracy. The application can timely and accurately detect whether the personnel wear the helmet correctly, which is of great significance for ensuring construction safety in practical work.
Subject
General Physics and Astronomy
Reference10 articles.
1. Safety helmet wearing detection based on image processing and machine learning;Li,2017
2. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks IEEE;Ren;Trans. Pattern Anal. Mach. Intell.,2017
3. Mask R-CNN IEEE;He,2018
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Classification of People Both Wearing Medical Mask and Safety Helmet;Engineering Cyber-Physical Systems and Critical Infrastructures;2023
2. Improved Helmet Detection Model Using YOLOv5;Intelligent Computing and Networking;2023