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mbariml: a curation pipeline for turning deep-sea imagery and video into object-detection training data

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Training data quantity and quality greatly affect object detection model performance, regardless of model architecture. When using object detection models on video and images from the deep sea, in which the objects of interest, primarily organisms, are sparse, faint, and hard to identify, incremental improvements to object detector performance may require an iterative approach to data labeling and management. This paper presents mbariml, a python-based video and image analysis pipeline built around the data labeling management process. mbariml uses an Ultralytics YOLO detection model, runs it

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