Sensor-Based Self-Rescue Alarm System for the Prevention of Child Drowning

Deng, Yan; Zhou, Tianhong

doi:10.1109/ihmsc52134.2021.00046

Cited by 1 publication

(2 citation statements)

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“…Therefore, the YOLOv5s of the series of YOLOv5 eventually stands out as an optimal model. The mAP of the proposed method reaches 89% and the FPS is 45, which is higher than the method proposed by Beijing University of Technology in 2020, whose FPS is around 30 and accuracy is 84% [16] .…”

Section: Discussionmentioning

confidence: 56%

“…Currently, research on infant drowning automatic detection is still in its infancy. Gao [16] used SIFT algorithm and nine-block grid feature extraction algorithm to decide infant drowning or not, with an accuracy rate of 84%; however, the accuracy rate was only obtained by using an infant model instead of a real live one. Therefore, practical feasibility of the method remains a question.…”

Section: Related Workmentioning

confidence: 99%

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

Mei

Zhang

et al. 2023

J. Soc. Comput.

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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 exerts negative effects on the detection of drowning. Aiming at this problem, a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance. Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets.A part of the datasets is downscaled or enlarged to enhance generalization ability of the model. On this basis, advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world. Supervised learning experiments are carried out, model test results show that mean Average Precision (mAP) of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%; the former can process merely 6 frames of videos per second with the precision of only 62.04%, while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%. The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance, which is of great application value to reduce the accidents in swimming pools.

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Section: Discussionmentioning

confidence: 56%

Section: Related Workmentioning

confidence: 99%