IBURD：用于水下机器人检测的图像混合
IBURD: Image Blending for Underwater Robotic Detection
AI总结：
提出图像混合流水线IBURD，生成逼真水下海洋垃圾合成图像及像素级标注，解决数据稀缺问题，经定量与机器人评估验证其有效性。
摘要：
我们提出了一种图像混合流水线IBURD，它能够生成逼真的合成图像，以辅助训练用于水下自主航行器（AUVs）执行海洋垃圾检测任务的深度检测器。具体而言，IBURD利用垃圾物体的源图像及其标注，以及海洋环境的目标背景图像，生成水下垃圾图像及其像素级标注。通过泊松编辑和风格迁移技术，IBURD甚至能够将透明物体稳健地混合到任意背景中，并利用目标背景图像的模糊度度量自动调整混合图像的风格。这些在真实水下背景中生成的海洋垃圾图像解决了深度学习视觉算法在具有挑战性的水下环境中面临的数据稀缺和数据多样性问题，并能够支持AUVs用于环境清理任务。对IBURD的定量评估和机器人评估均证明了所提方法在海洋垃圾机器人检测中的有效性。
英文摘要：
We present an image blending pipeline, \textit{IBURD}, that creates realistic synthetic images to assist in the training of deep detectors for use on underwater autonomous vehicles (AUVs) for marine debris detection tasks. Specifically, IBURD generates both images of underwater debris and their pixel-level annotations, using source images of debris objects, their annotations, and target background images of marine environments. With Poisson editing and style transfer techniques, IBURD is even able to robustly blend transparent objects into arbitrary backgrounds and automatically adjust the style of blended images using the blurriness metric of target background images. These generated images of marine debris in actual underwater backgrounds address the data scarcity and data variety problems faced by deep-learned vision algorithms in challenging underwater conditions, and can enable the use of AUVs for environmental cleanup missions. Both quantitative and robotic evaluations of IBURD demonstrate the efficacy of the proposed approach for robotic detection of marine debris.
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