Generative Adversarial Networks and Data Clustering for Likable Drone Design

Lee J. Yamin, Jessica R. Cauchard

Research output: Contribution to journalArticlepeer-review

Abstract

Novel applications for human-drone interaction demand new design approaches, such as social drones that need to be perceived as likable by users. However, given the complexity of the likability perception process, gathering such design information from the interaction context is intricate. This work leverages deep learning-based techniques to generate novel likable drone images. We collected a drone image database ((Formula presented.)) applicable for design research and assessed the drone’s likability ratings in a user study ((Formula presented.)). We employed two clustering methodologies: 1. likability-based, which resulted in non-likable, neutral, and likable drone clusters; and 2. feature-based (VGG, PCA), which resulted in drone clusters characterized by visual similarity; both clustered using the K-means algorithm. A characterization process identified three drone features: colorfulness, animal-like representation, and emotional expressions through facial features, which affect drone likability, going beyond prior research. We used the likable drone cluster ((Formula presented.)) for generating new images using StyleGAN2-ADA and addressed the dataset size limitation using specific configurations and transfer learning. Our results were mitigated due to the dataset size; thus, we illustrate the feasibility of our approach by generating new images using the original database. Our findings demonstrate the effectiveness of Generative Adversarial Networks (GANs) exploitation for drone design, and to the best of our knowledge, this work is the first to suggest GANs for such application.

Original languageAmerican English
Article number6433
JournalSensors
Volume22
Issue number17
DOIs
StatePublished - 1 Sep 2022

Keywords

  • data clustering
  • deep learning
  • drone design
  • generative adversarial networks
  • human-drone interaction

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry

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