Context-Matched Collage Generation for Underwater Invertebrate Detection

TL;DR

This paper introduces Context Matched Collages, a data augmentation technique that combines background and annotated images to improve underwater invertebrate detection with limited data, achieving state-of-the-art results.

Contribution

It presents a novel collage-based augmentation method leveraging context labels to enhance training data for underwater object detection.

Findings

Improved detection performance on DUSIA dataset.

Achieved state-of-the-art results with limited annotations.

Effective across multiple object detectors.

Abstract

The quality and size of training sets often limit the performance of many state of the art object detectors. However, in many scenarios, it can be difficult to collect images for training, not to mention the costs associated with collecting annotations suitable for training these object detectors. For these reasons, on challenging video datasets such as the Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), budgets may only allow for collecting and providing partial annotations. To aid in the challenges associated with training with limited and partial annotations, we introduce Context Matched Collages, which leverage explicit context labels to combine unused background examples with existing annotated data to synthesize additional training samples that ultimately improve object detection performance. By combining a set of our generated collage images with the original training set, we see improved performance using three different object detectors on DUSIA, ultimately achieving state of the art object detection performance on the dataset.