| 引用本文: | 曾纪钧,温柏坚,梁哲恒.基于深度学习的安全帽识别算法研究与模型训练[J].电力系统保护与控制,2021,49(21):107-112. |
| ZENG Jijun,WEN Bojian,LIANG Zheheng.Research and model training of helmet recognition algorithm based on deep learning[J].Power System Protection and Control,2021,49(21):107-112 |
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| 摘要: |
| 针对作业人员不按规定佩戴安全帽和非作业人员误入作业现场的问题，设计了基于深度学习的安全帽和语音识别智能终端算法。对于安全帽的检测，采用了人体关键点检测模型和基于深度学习的YOLO3算法。将智能摄像头得到的视频文件，先利用人体关键点模型提取现场人员图像，再结合YOLO3算法检测现场作业人员佩戴安全帽的情况，对于未正确佩戴安全帽的人员发出告警信息。通过模型训练验证了所提模型的实用性。 |
| 关键词: 深度学习 安全帽 YOLO3算法 模型训练 |
| DOI：DOI: 10.19783/j.cnki.pspc.210104 |
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| 基金项目:南方电网公司科技项目资助(037800KK52190006) |
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| Research and model training of helmet recognition algorithm based on deep learning |
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ZENG Jijun, WEN Bojian, LIANG Zheheng
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(Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)
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| Abstract: |
| There is a problem of workers who don't wear safety helmets as required as well as non-operating personnel entering a job site by mistake. Thus a deep learning-based safety helmet and voice recognition intelligent terminal algorithm is designed. First, for the detection of helmets, we use a human body key point detection model and a YOLO3 algorithm based on deep learning. The video file obtained by the smart camera is first used to extract the images of the on-site personnel using the human body key point model, and then the YOLO3 algorithm is applied to detect the situation of the on-site workers wearing helmets and send out warning messages for those who do not wear the helmet correctly. Finally, the practicality of the proposed model is verified through model training.
This work is supported by the Science and Technology Project of China Southern Power Grid Company Limited (No. 037800k52190006). |
| Key words: deep learning safety helmet YOLO3 algorithm model training |
