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

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