论文标题
从水下图像中的多物种海草检测和分类
Multi-species Seagrass Detection and Classification from Underwater Images
论文作者
论文摘要
使用配备自定义相机有效载荷的潜水员或机器人进行的水下调查可以产生大量图像。在时间和成本方面，对这些图像提取生态数据的手动审查是过时的，因此可以使用机器学习解决方案来自动化此过程。在本文中，我们基于深卷积神经网络引入了海草的多物种检测器和分类器（达到92.4％的总体准确性）。我们还引入了一种简单的方法，以半自动标记图像贴片，从而最大程度地减少手动标记要求。我们将在本研究中收集的数据集以及代码和预培训的模型公开描述和发布，以复制我们的实验：https：//github.com/csiro-robotics/deepseagrass
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecological data is prohibitive in terms of time and cost, thus providing strong incentive to automate this process using machine learning solutions. In this paper, we introduce a multi-species detector and classifier for seagrasses based on a deep convolutional neural network (achieved an overall accuracy of 92.4%). We also introduce a simple method to semi-automatically label image patches and therefore minimize manual labelling requirement. We describe and release publicly the dataset collected in this study as well as the code and pre-trained models to replicate our experiments at: https://github.com/csiro-robotics/deepseagrass
