This paper presents a labeling methodology for marine life data using a weakly supervised learning framework. The methodology iteratively trains a deep learning model using non-expert labels obtained from crowdsourcing. This approach enables us to converge on a labeled image dataset through multiple training and production loops that leverage crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset consists of 10,505 images that were point annotated by NOAA biologists. This dataset allows us to validate the accuracy of our labeling process. We also apply our algorithm and methodology to a second dataset consisting of 3,968 completely unlabeled images. These image categories are challenging to label, such as sponges. Qualitatively, our results indicate that training with a tiny subset and iterating on those results allows us to converge to a large, highly annotated dataset with a small number of iterations. To demonstrate the effectiveness of our methodology quantitatively, we tabulate the mean average precision (mAP) of the model as the number of iterations increases.

中文翻译：

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一种用于标注海洋生物图像的迭代标注方法

本文介绍了一种使用弱监督学习框架的海洋生物数据标记方法。该方法使用从众包中获得的非专家标签迭代训练深度学习模型。这种方法使我们能够通过利用众包接口的多个训练和生产循环来集中在标记的图像数据集上。我们在使用 Seabed 自主水下航行器收集的两组独立图像数据上展示了我们的算法及其结果。第一个数据集包含 10,505 张图像，这些图像由 NOAA 生物学家进行了点注释。该数据集使我们能够验证标记过程的准确性。我们还将我们的算法和方法应用于由 3,968 个完全未标记的图像组成的第二个数据集。这些图像类别很难标记，例如海绵。定性地，我们的结果表明，使用一个很小的子集进行训练并对这些结果进行迭代使我们能够通过少量迭代收敛到一个大型的、高度注释的数据集。为了定量地证明我们的方法的有效性，我们将随着迭代次数的增加模型的平均精度 (mAP) 制表。