Speaker
Description
Artificial intelligence (AI), particularly computer vision algorithms like YOLO, has become standard for automated organism detection. However, AI complexity remains a barrier for non-specialist researchers, limiting adoption in marine biodiversity studies. This project presents iSEA (Intelligent Seafloor & Animal Image Annotator), an interactive Python-based GUI that integrates YOLO for automated detection and classification of marine fauna in underwater images. The intuitive interface allows researchers to train custom models without programming expertise, reducing analysis time while improving dataset quality through human-in-the-loop refinement. Real-time processing enables use aboard research vessels as a decision support tool during expeditions. A seafloor classification module using InceptionV3 enables dual-mode operation, allowing simultaneous fauna detection and habitat characterization within the same video transect. The platform was tested on ROV footage from deep-sea coral habitats in Brazil's Campos Basin and the models trained were evaluated using standard metrics (F1, mAP, precision, recall). Designed to be user friendly, iSEA bridges the gap between technical AI knowledge and practical marine science applications, accelerating image analysis and ultimately enhancing conservation strategies for vulnerable ecosystems.