Automatic Real-Time Detection of Infant Drowning Using YOLOv5 and Faster R-CNN Models Based on Video Surveillance

Automatic Real-Time Detection of Infant Drowning Using YOLOv5 and Faster R-CNN Models Based on Video Surveillance

Infant drowning has occurred frequently in swimming pools recent years, which motivates the research on automatic real-time detection of the accident. Unlike youths or adults, swimming infants are small in terms of size and motion range, and unable to send out distress signals in emergencies, which...

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Bibliographic Details
Main Authors: Qianen He, Zhiqiang Mei, Huisheng Zhang, Xiuying Xu
Format: Article
Language:English
Published: Tsinghua University Press 2023-03-01
Series:Journal of Social Computing
Subjects:
infant drowning detection
yolov5
faster r-cnn
video surveillance
supervised learning
Online Access:https://www.sciopen.com/article/10.23919/JSC.2023.0006

Internet

https://www.sciopen.com/article/10.23919/JSC.2023.0006

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