Document Type
Journal Article
Publication Title
Ecological Informatics
Publisher
Elsevier
School
School of Science
RAS ID
54630
Funders
Australian Government Research Training Program (RTP)
Edith Cowan University
Abstract
Seagrasses provide a wide range of ecosystem services in coastal marine environments. Despite their ecological and economic importance, these species are declining because of human impact. This decline has driven the need for monitoring and mapping to estimate the overall health and dynamics of seagrasses in coastal environments, often based on underwater images. However, seagrass detection from underwater digital images is not a trivial task; it requires taxonomic expertise and is time-consuming and expensive. Recently automatic approaches based on deep learning have revolutionised object detection performance in many computer vision applications, and there has been interest in applying this to automated seagrass detection from imagery. Deep learning–based techniques reduce the need for hardcore feature extraction by domain experts which is required in machine learning-based techniques. This study presents a YOLOv5-based one-stage detector and an EfficientDetD7–based two-stage detector for detecting seagrass, in this case, Halophila ovalis, one of the most widely distributed seagrass species. The EfficientDet-D7–based seagrass detector achieves the highest mAP of 0.484 on the ECUHO-2 dataset and mAP of 0.354 on the ECUHO-1 dataset, which are about 7% and 5% better than the state-of-the-art Halophila ovalis detection performance on those datasets, respectively. The proposed YOLOv5-based detector achieves an average inference time of 0.077 s and 0.043 s respectively which are much lower than the state-of-the-art approach on the same datasets.
DOI
10.1016/j.ecoinf.2023.102047
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Noman, M. K., Islam, S. M. S., Abu-Khalaf, J., Jalali, S. M. J., & Lavery, P. (2023). Improving accuracy and efficiency in seagrass detection using state-of-the-art AI techniques. Ecological Informatics, 76, article 102047. https://doi.org/10.1016/j.ecoinf.2023.102047