Edge Deployable Online Domain Adaptation for Underwater Object Detection

Collecting and curating data plays a crucial role in environmental surveying. In order to gather meaningful samples, it is often necessary to develop a real-time data curation system that processes data on-the-fly and enriches it with validated models from domain experts. One area where this is particularly important is underwater marine surveys, where the vastness of the sea requires human interaction in the curation process to focus on relevant areas for exploration. Additionally, ongoing surveys are susceptible to poor performance due to data drift, which hinders the ability to provide valuable feedback for guiding data collection. While recent advancements have shown promise in addressing these challenges, they often overlook the practical constraints associated with remote data collection, such as limited processing power and latency. To overcome these limitations, this paper proposes a real-time system that adapts to data drift and enables the recording of uncertain samples for further processing and analysis on shore. The results of our approach demonstrate a remarkable improvement in species recognition, achieving an almost 18% improvement compared to the best baseline method in unseen areas. Importantly, this improvement is achieved while meeting the real-time requirements of surveys and consuming only 15W of power. By effectively addressing the challenges of underwater data drift, our proposed approach provides an efficient and effective solution for environmental surveys.