We are thrilled to announce that our esteemed partners, ICM- CSIC, Stazione Zoologica Anton Dohrn and University of Gothenburg have proceed to a new achievement within the context of DIGI4ECO project.
The article “Automated species classification and counting by deep-sea mobile crawler platforms using YOLO” has been published at “Ecological Informatics”, by ELSEVIER.
The authors of the article are: Luciano Ortenzi, Jacopo Aguzzi, Corrado Costa, Simone Marini, Daniele D’Agostino , Laurenz Thomsen , Fabio C. De Leo , Paulo V. Correa, Damianos Chatzievangelou and its respective DOI is the following: https://doi.org/10.1016/j.ecoinf.2024.102788
The abstract and the keywords of the publication can be found below:
Abstract:
Edge computing on mobile marine platform is paramount for automated ecological monitoring. The goal of demonstrating the computational feasibility of an Artificial Intelligence (AI)-powered camera for fully automated real-time species-classification on deep-sea crawler platforms was searched by running You-Only-Look-Once (YOLO) model on an edge computing device (NVIDIA Jetson Nano), to evaluate the achievable animal detection performances, execution time and power consumption, using all the available cores. We processed a total of 337 rotating video scans (∼180°), taken during approximately 4 months in 2022 at the methane hydrates site of Barkley Canyon (Vancouver Island; BC; Canada), focusing on three abundant species (i.e., Sablefish Anoplopoma fimbria, Hagfish Eptatretus stoutii, and Rockfish Sebastes spp.). The model was trained on 1926 manually annotated video frames and showed high detection test performances in terms of accuracy (0.98), precision (0.98), and recall (0.99). The trained model was then applied on 337 videos.
In 288 videos we detected a total of 133 Sablefish, 31 Hagfish, and 321 Rockfish nearly in real-time (about 0.31 s/image) with very low power consumption (0.34 J/image). Our results have broad implications on intelligent ecological monitoring. Indeed, YOLO model can meet operational-autonomy criteria for fast image processing with limited computational and energy loads.
Keywords:
Edge-computing, Ecological monitoring, Artificial intelligence, Machine learning, Cold-seep, Robotic platforms, Benthic fish
You can reach the full article following this link: https://www.sciencedirect.com/science/article/pii/S1574954124003303?via%3Dihub