用扩散模型生成的合成数据，显著提升水下目标检测泛化能力。
Syn2Real Domain Generalization for Underwater Mine-like Object Detection Using Side-Scan Sonar
- 用扩散模型生成带噪声的合成声呐图像，增强训练数据
- 结合合成与真实数据训练，平均精度提升约60%
- 适合数据稀缺场景下的水下探测任务
基于深度学习的水下地雷检测受限于真实数据稀缺，导致模型过拟合，难以泛化到未见数据。本文提出一种基于扩散模型的Syn2Real（合成到真实）域泛化方法，利用DDPM和DDIM模型生成带噪声的合成数据。实验表明，即使合成图像不够逼真，其残余噪声也能帮助模型适应真实数据中固有的噪声与高变异性。当基线Mask-RCNN模型在合成数据与原始数据混合的训练集上训练时，平均精度（AP）相比仅使用原始数据训练提升了约60%。这一显著提升证明了该方法在水下地雷检测任务中的有效性。
原文摘要 · Abstract (English)
Underwater mine detection with deep learning suffers from limitations due to the scarcity of real-world data. This scarcity leads to overfitting, where models perform well on training data but poorly on unseen data. This paper proposes a Syn2Real (Synthetic to Real) domain generalization approach using diffusion models to address this challenge. We demonstrate that synthetic data generated with noise by DDPM and DDIM models, even if not perfectly realistic, can effectively augment real-world samples for training. The residual noise in the final sampled images improves the model's ability to generalize to real-world data with inherent noise and high variation. The baseline Mask-RCNN model when trained on a combination of synthetic and original training datasets, exhibited approximately a 60% increase in Average Precision (AP) compared to being trained solely on the original training data. This significant improvement highlights the potential of Syn2Real domain generalization for underwater mine detection tasks.
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