Abstract
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.
Abstract (translated)
我们提出了一种图像融合流水线\textit{IBURD}，它生成逼真的合成图像，以帮助训练用于海底自主水下航行器（AUV）的海洋垃圾检测任务的深度探测器。具体而言，IBURD 使用物体碎片源图、它们的标注和目标背景图来生成海底垃圾图像及其像素级注释。通过使用泊松编辑和风格转换技术，IBURD 能够将透明对象无缝融合到任意背景中，并且能够自动根据目标背景图的模糊度调整合成图像的风格。这些在实际水下环境中生成的海洋碎片图像可以解决深度学习视觉算法在复杂水下条件下面临的缺乏多样性和数据稀缺性的问题，并能促进AUV用于环境清理任务的应用。IBURD 的定量和机器人测试评估证明了该方法在机器人检测海洋垃圾方面的有效性。
URL
https://arxiv.org/abs/2502.17706
