论文标题
伽玛：专注海洋碎片检测的生成增强
GAMMA: Generative Augmentation for Attentive Marine Debris Detection
论文作者
论文摘要
我们提出了一种有效而生成的增强方法，以解决水下碎片数据以进行视觉检测的不足问题。我们使用Cyclegan作为数据增强技术，将陆地塑料的大量数据转换为水下式图像。先前的工作只是专注于增强或增强现有数据，从而为数据集增加了偏见。与我们的技术相比，该技术设计了变化，将其他空气塑料数据转化为海洋背景。我们还提出了一种使用注意机制来检测水下碎屑检测的新型结构。我们的方法有助于仅关注图像的相关实例，从而增强检测器性能，在使用自动水下汽车（AUV）检测海洋碎片时，这是高度义务的。我们使用我们的方法进行了广泛的实验，以进行海洋碎片检测。定量和定性结果证明了我们框架的潜力，显着优于最新方法。
We propose an efficient and generative augmentation approach to solve the inadequacy concern of underwater debris data for visual detection. We use cycleGAN as a data augmentation technique to convert openly available, abundant data of terrestrial plastic to underwater-style images. Prior works just focus on augmenting or enhancing existing data, which moreover adds bias to the dataset. Compared to our technique, which devises variation, transforming additional in-air plastic data to the marine background. We also propose a novel architecture for underwater debris detection using an attention mechanism. Our method helps to focus only on relevant instances of the image, thereby enhancing the detector performance, which is highly obliged while detecting the marine debris using Autonomous Underwater Vehicle (AUV). We perform extensive experiments for marine debris detection using our approach. Quantitative and qualitative results demonstrate the potential of our framework that significantly outperforms the state-of-the-art methods.
