Quality-Driven and Diversity-Aware Sample Expansion for Robust Marine Obstacle Segmentation

TL;DR

This paper introduces a novel data augmentation pipeline that enhances marine obstacle segmentation robustness by generating diverse, high-quality synthetic training samples at inference time, without retraining the diffusion model.

Contribution

It proposes a quality-driven, diversity-aware sample expansion method combining a style bank and adaptive sampling to improve segmentation robustness in marine environments.

Findings

Improved segmentation performance across multiple backbones.

Enhanced visual variation in rare and texture-sensitive classes.

Consistent gains on marine obstacle benchmarks.

Abstract

Marine obstacle detection demands robust segmentation under challenging conditions, such as sun glitter, fog, and rapidly changing wave patterns. These factors degrade image quality, while the scarcity and structural repetition of marine datasets limit the diversity of available training data. Although mask-conditioned diffusion models can synthesize layout-aligned samples, they often produce low-diversity outputs when conditioned on low-entropy masks and prompts, limiting their utility for improving robustness. In this paper, we propose a quality-driven and diversity-aware sample expansion pipeline that generates training data entirely at inference time, without retraining the diffusion model. The framework combines two key components:(i) a class-aware style bank that constructs high-entropy, semantically grounded prompts, and (ii) an adaptive annealing sampler that perturbs early conditioning, while a COD-guided proportional controller regulates this perturbation to boost diversity without compromising layout fidelity. Across marine obstacle benchmarks, augmenting training data with these controlled synthetic samples consistently improves segmentation performance across multiple backbones and increases visual variation in rare and texture-sensitive classes.