FEB-YOLOv8: A multi-scale lightweight detection model for underwater object detection

Yuyin Zhao, Fengjie Sun and Xuewen Wu

PLOS ONE, 2024, vol. 19, issue 9, 1-19

Abstract: Underwater object detection plays a crucial role in safeguarding and exploiting marine resources effectively. Addressing the prevalent issues of limited storage capacity and inadequate computational power in underwater robots, this study proposes FEB-YOLOv8, a novel lightweight detection model. FEB-YOLOv8, rooted in the YOLOv8 framework, enhances the backbone network by refining the C2f module and introducing the innovative P-C2f module as a replacement. To compensate for any potential reduction in detection accuracy resulting from these modifications, the EMA module is incorporated. This module augments the network’s focus on multi-scale information, thus boosting its feature extraction capabilities. Furthermore, inspired by Bi-FPN concepts, a new feature pyramid network structure is devised, achieving an optimal balance between model lightness and detection precision. The experimental results on the underwater datasets DUO and URPC2020 reveal that our FEB-YOLOv8 model enhances the mAP by 1.2% and 1.3% compared to the baseline model, respectively. Moreover, the model’s GFLOPs and parameters are lowered to 6.2G and 1.64M, respectively, marking a 24.39% and 45.51% decrease from the baseline model. These experiments validate that FEB-YOLOv8, by harmonizing lightness with accuracy, presents an advantageous solution for underwater object detection tasks.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0311173 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 11173&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0311173

DOI: 10.1371/journal.pone.0311173

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().