Research on Dataset Generation and Monitoring of Generative AI for Drowning Warning System

Abstract

In order to address the scarcity of images in real-world drowning datasets, this research aims to create an intelligent system that can generate a large number of drowning datasets by optimizing AI image generation algorithms. The system will gradually be used to compensate for the shortage of rare real-world drowning datasets. This method is not only based on traditional AI image generation steps but also optimizes the engine framework to create more drowning datasets. For the key elements of drowning, on the one hand, different filters, especially blue and green filters, will be added to distinguish the color differences between underwater and above water. On the other hand, the framework structure of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models will be optimized to further reduce system computation. At the same time, the detection of drowning swimmers in the system has become clearer. It can greatly improve the performance and efficiency of drowning monitoring algorithms. The drowning dataset generated by AI can describe different real-world drowning processes and perfectly adapt to different emergency scenarios. This method is also applicable to dangerous behaviors where the process is difficult to record.

Keywords

• Drowning datasets
• engine framework
• generative adversarial networks
• variational autoencoders