Modelling of Innovative Approaches for Drowning Prevention: Customized CNNs and Optimization of Binary Chimps for Early Detection
DOI:
https://doi.org/10.71426/jasm.v1.i1.pp1-8Keywords:
Drowning detection, Binary chimp, Residual block, Data augmentation, Convolutional Neural Networks (CNNs)Abstract
Among the top five global causes of death for children ages one to fourteen is drowning. Drowning is the third most common cause of unintentional mortality, according to data from the World Health Organization (WHO). Existing drowning detection systems including the wearable and camera based approaches have proven to face various limitations such as restricted field view, environmental sensitivity, delayed responses and limited applicability in real world. Moreover, many existing approaches only focus on abnormal motion instead of accurately identifying the drowning behavior which is generally subtle and motionless. These limitations highlight the need for more reliable, feasible and real time drowning system. It is becoming inevitable to design a drowning detection system to protect swimmers, especially kids. This research provides an early drowning detection method based on computer vision and deep learning approach. Using a public available dataset we trained Residual Block 3 and Residual Block 4 of convolutional neural networks. The proposed architecture achieved 97.6% accuracy with a training time of 3.9137 seconds after feature optimization, which demonstrated a remarkable performance for both prediction precision and computational capacity.
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