Abstract
Swimming pool intelligent assisted drowning detection is an important research content in the field of drowning rescue. A large number of scholars track drowning targets in real time through underwater intelligent monitoring system, and use it to build a reliable swimming pool intelligent assisted drowning detection model to reduce the risk of drowning. For the complex underwater environment of the swimming pool, the previous detection model has been difficult to adapt to the practical demand. In this regard, based on the summary of the previous swimming pool intelligent assisted drowning detection models and the computer feature pyramid networks, the feature stratification of the swimming pool intelligent assisted drowning detection image is completed, and then the final swimming pool intelligent assisted drowning detection results are obtained through the YOLO principle. After analysis, it is confirmed that the accuracy rate of swimming pool intelligent assisted drowning detection of this method is significantly improved, which can provide effective data theoretical guidance for swimming pool intelligent assisted drowning rescue and has significant practical advantages.
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