Improving rare-class detection in deep-sea imagery via generative augmentation with stable diffusion.

Megabenthos play a critical role in maintaining deep-sea ecosystem stability, making accurate detection important for deep-sea conservation. However, the high cost of deep-sea exploration and the long-tailed distribution of available datasets lead to severe data scarcity for rare species, limiting deep-sea benthos detection. To address this challenge, we propose a data augmentation framework based on Stable Diffusion (SD) and ControlNet. Specifically, we fine-tune a pretrained SD model using Low-Rank Adaptation (LoRA) to synthesize images of rare benthos, and leverage ControlNet to composite the generated targets into deep-sea backgrounds with controllable layouts and automatic bounding-box annotation. We constructed two megabenthos datasets collected using an optically tethered underwater vehicle (OTV) and an autonomous underwater vehicle (AUV), covering 16 biological categories; data augmentation was applied to 7 rare species with the fewest samples. The generated images achieved a Fréchet Inception Distance (FID) of 117.11 and an Inception Score (IS) of 4.97. When combined with real data for RT-DETR training, the augmentation strategy increased the AP