Improving underwater object classification: BC-ViT

Aidan Kurz; Ethan Adams; Arthur C Depoian II; Hae Jin Kim; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2619134

30 May 2022 Improving underwater object classification: BC-ViT

Aidan Kurz, Ethan Adams, Arthur C Depoian II, Hae Jin Kim, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 12118, Ocean Sensing and Monitoring XIV; 121180G (2022) https://doi.org/10.1117/12.2619134
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

As one of the classic fields of computer vision, image classification is a topic that has expanded exponentially in terms of usability and accuracy in recent years. With the rapid progression of deep learning, as well as the introduction and advancement of techniques such as convolutional neural networks and vision transformers, image classification has been elevated to levels only theoretical until modern times. This paper presents an improved method of object classification using a combination of vision transformers and multilayer convolutional neural networks with specific application to underwater environments. In comparison to previous underwater object classification algorithms, the proposed network classifies images with higher accuracy, shorter training iterations, and deployable parameters.

Conference Presentation

Citation Download Citation

Aidan Kurz, Ethan Adams, Arthur C Depoian II, Hae Jin Kim, Colleen P. Bailey, and Parthasarathy Guturu "Improving underwater object classification: BC-ViT", Proc. SPIE 12118, Ocean Sensing and Monitoring XIV, 121180G (30 May 2022); https://doi.org/10.1117/12.2619134

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