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There is a need to introduce remote disaster monitoring camera systems that utilize AI technology. This is especially the point where disasters have become larger and more frequent in recent years. The challenge to realize this system is to acquire images with cameras and to transmit this information stably and quickly. I then turned my attention to the CAE(Convolutional AutoEncoder). The reason for using CAE was to reduce the amount of data compared to images captured by cameras and to improve security. In this paper, we use this CAE to design an AI model that integrates an image recognition model for image restoration verification and disaster detection, and verify its performance.

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Performance Evaluation of a Combined Convolutional AutoEncoder and Image Recognition Model for Large Scale Images

  • 会议论文

  • 发表日期：

    2025年

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  • Takuho Myojin,
  • Yukinobu Hoshino

摘要

There is a need to introduce remote disaster monitoring camera systems that utilize AI technology. This is especially the point where disasters have become larger and more frequent in recent years. The challenge to realize this system is to acquire images with cameras and to transmit this information stably and quickly. I then turned my attention to the CAE(Convolutional AutoEncoder). The reason for using CAE was to reduce the amount of data compared to images captured by cameras and to improve security. In this paper, we use this CAE to design an AI model that integrates an image recognition model for image restoration verification and disaster detection, and verify its performance.