Outputs (2)

Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network (2023)
Journal Article
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

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,... Read More about Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network.

Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery (2023)
Journal Article
Zhao, N., Huang, B., Yang, J., Radenkovic, M., & Chen, G. (2023). Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery. IEEE Geoscience and Remote Sensing Letters, 20, Article 1500605. https://doi.org/10.1109/lgrs.2023.3243902

Oceanic eddy is the ubiquitous ocean flow phenomenon, which has been the key factor in the transportation of ocean energy and materials. Consequently, oceanographic understanding can be enhanced by the intelligent identification of eddy. State-of-the... Read More about Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery.