Autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm

Abstract

Image-based sensing of jellyfish is important as they can cause great damage to the fisheries and seaside facilities and need to be properly controlled. In this paper, we present a deep-learning-based technique to generate a synthetic image of the jellyfish easily with autoencoder-combined generative adversarial networks. The proposed system can easily generate simple images with a smaller number of data sets compared with other generative networks. The generated output showed high similarity with the real-image data set. The application using a fully convolutional network and regression network to estimate the size of the jellyfish swarm was also demonstrated, and showed high accuracy during the estimation test.

Keywords

• Autoencoder
• generative adversarial networks
• jellyfish swarm
• fully convolutional network
• regression