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Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network

Huang, Baoxiang; Chen, Ge; Zhang, Hongfeng; Hou, Guojia; Radenkovic, Milena

Authors

Baoxiang Huang

Ge Chen

Hongfeng Zhang

Guojia Hou



Abstract

Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain.

Citation

Huang, B., Chen, G., Zhang, H., Hou, G., & Radenkovic, M. (2023). Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network. Science of the Total Environment, 878, Article 162826. https://doi.org/10.1016/j.scitotenv.2023.162826

Journal Article Type Article
Acceptance Date Mar 9, 2023
Online Publication Date Mar 28, 2023
Publication Date Jun 20, 2023
Deposit Date May 26, 2023
Publicly Available Date Mar 29, 2024
Journal Science of the Total Environment
Print ISSN 0048-9697
Electronic ISSN 1879-1026
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 878
Article Number 162826
DOI https://doi.org/10.1016/j.scitotenv.2023.162826
Keywords Marine pollution, Deep sea debris, Seafloor, Deep learning-based detection method
Public URL https://nottingham-repository.worktribe.com/output/19779203
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0048969723014420?via%3Dihub

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