Cédric M. John

1 month ago | dehai.org | Tom Harper |Ali Bilgiç |Gemma Ware |Cédric M. John

Donald Trump’s decision to pause his tariff hike on all but China took much of the world by surprise. The U.S. president had insisted until the morning of April 9 that everything was going to plan and that the massive falls in share prices were just “medicine” that was needed if he were to make America great again. So when he announced his decision at lunchtime the same day that he would hold to 10% tariffs across the board it was something of a bombshell.

Sep 3, 2024 | nature.com | Cédric M. John |Chen Zhang |Tristan Salles |Jody M. Webster |Rohitash Chandra |Kate Whitton | +1 more

Understanding and preserving the deep sea ecosystems is paramount for marine conservation efforts. Automated object (deep-sea biota) classification can enable the creation of detailed habitat maps that not only aid in biodiversity assessments but also provide essential data to evaluate ecosystem health and resilience. Having a significant source of labelled data helps prevent overfitting and enables training deep learning models with numerous parameters. In this paper, we contribute to the establishment of a significant deep-sea remotely operated vehicle (ROV) image classification dataset with 3994 images featuring deep-sea biota belonging to 33 classes. We manually label the images through rigorous quality control with human-in-the-loop image labelling. Leveraging data from ROV equipped with advanced imaging systems, our study provides results using novel deep-learning models for image classification. We use deep learning models including ResNet, DenseNet, Inception, and Inception-ResNet to benchmark the dataset that features class imbalance with many classes. Our results show that the Inception-ResNet model provides a mean classification accuracy of 65%, with AUC scores exceeding 0.8 for each class.