Document Type

Journal Article

Publication Title

PLoS ONE

Volume

19

Issue

6

PubMed ID

38917071

Publisher

PLOS

School

School of Science

RAS ID

71521

Funders

Australian Government Research Training Program

Comments

Noman, M. K., Shamsul Islam, S. M., Jafar Jalali, S. M., Abu-Khalaf, J., & Lavery, P. (2024). BAOS-CNN: A novel deep neuroevolution algorithm for multispecies seagrass detection. Plos one, 19(6), e0281568. https://doi.org/10.1371/journal.pone.0281568

Abstract

Deep learning, a subset of machine learning that utilizes neural networks, has seen significant advancements in recent years. These advancements have led to breakthroughs in a wide range of fields, from natural language processing to computer vision, and have the potential to revolutionize many industries or organizations. They have also demonstrated exceptional performance in the identification and mapping of seagrass images. However, these deep learning models, particularly the popular Convolutional Neural Networks (CNNs) require architectural engineering and hyperparameter tuning. This paper proposes a Deep Neuroevolutionary (DNE) model that can automate the architectural engineering and hyperparameter tuning of CNNs models by developing and using a novel metaheuristic algorithm, named ‘Boosted Atomic Orbital Search (BAOS)’. The proposed BAOS is an improved version of the recently proposed Atomic Orbital Search (AOS) algorithm which is based on the principle of atomic model and quantum mechanics. The proposed algorithm leverages the power of the Lévy flight technique to boost the performance of the AOS algorithm. The proposed DNE algorithm (BAOS-CNN) is trained, evaluated and compared with six popular optimisation algorithms on a patch-based multi-species seagrass dataset. This proposed BAOS-CNN model achieves the highest overall accuracy (97.48%) among the seven evolutionary-based CNN models. The proposed model also achieves the state-of-the-art overall accuracy of 92.30% and 93.5% on the publicly available four classes and five classes version of the ‘DeepSeagrass’ dataset, respectively. This multi-species seagrass dataset is available at: https://ro.ecu.edu.au/datasets/141/.

DOI

10.1371/journal.pone.0281568

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Download

Share

 
COinS