Underwater Mediterranean image analysis based on the compute continuum paradigm

• Published in: Future generation computer systems 2025-01, Vol.162, p.107481, Article 107481
• Main Authors: Ferrari, Michele, D’Agostino, Daniele, Aguzzi, Jacopo, Marini, Simone
• Format: Article
• Language: English
• Subjects: Compute continuum; Object detection
• ISSN: 0167-739X
• DOI: 10.1016/j.future.2024.107481

Human activity depends on the oceans for food, transportation, leisure, and many more purposes. Oceans cover 70% of the Earth’s surface, but most of them are unknown to humankind. This is the reason why underwater imaging is a valuable resource asset to Marine Science. Images are acquired with observing systems, e.g. autonomous underwater vehicles or underwater observatories, that presently transmit all the raw data to land stations. However, the transfer of such an amount of data could be challenging, considering the limited power supply and transmission bandwidth of these systems. In this paper, we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing for effective management of the full processing pipeline according to the Compute Continuum paradigm. •We present an object detection pipeline for Marine Science based on the Compute Continuum paradigm.•The Object detector has been trained for Mediterranean fishes, while related works normally consider tropical fishes.•The pipeline runs on the Jetson Nano board to reduce transmitted data size.•The work analyzes the execution time and power consumption of YoloV3, ULO, and Ulo Tiny.