DeepSea Monitoring Group, GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstraße 1-3, 24148, Kiel, Germany.
Institute of Geosciences, Kiel University, Ludewig-Meyn-Str. 10-12, 24118, Kiel, Germany.
Sci Rep. 2023 May 23;13(1):8350. doi: 10.1038/s41598-023-35518-5.
Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring of megabenthic fauna, flora and the marine ecosystem, traditional labor-intensive manual approaches for analyzing them are neither feasible nor scalable. Therefore, machine learning has been proposed as a solution, but training the respective models still requires substantial manual annotation. Here, we present an automated image-based workflow for Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast). The workflow significantly reduces the required annotation effort by automating the detection of anomalous superpixels, which are regions in underwater images that have unusual properties relative to the background seafloor. The bounding box coordinates of the detected anomalous superpixels are proposed as a set of weak annotations, which are then assigned semantic morphotype labels and used to train a Faster R-CNN object detection model. We applied this workflow to example underwater images recorded during cruise SO268 to the German and Belgian contract areas for Manganese-nodule exploration, within the Clarion-Clipperton Zone (CCZ). A performance assessment of our FaunD-Fast model showed a mean average precision of 78.1% at an intersection-over-union threshold of 0.5, which is on a par with competing models that use costly-to-acquire annotations. In more detail, the analysis of the megafauna detection results revealed that ophiuroids and xenophyophores were among the most abundant morphotypes, accounting for 62% of all the detections within the surveyed area. Investigating the regional differences between the two contract areas further revealed that both megafaunal abundance and diversity was higher in the shallower German area, which might be explainable by the higher food availability in form of sinking organic material that decreases from east-to-west across the CCZ. Since these findings are consistent with studies based on conventional image-based methods, we conclude that our automated workflow significantly reduces the required human effort, while still providing accurate estimates of megafaunal abundance and their spatial distribution. The workflow is thus useful for a quick but objective generation of baseline information to enable monitoring of remote benthic ecosystems.
近年来，光学水下成像技术的进步使得在科学考察中能够获取大量高分辨率海底图像。虽然这些图像为无损伤监测大型底栖动物、植物和海洋生态系统提供了有价值的信息，但传统的、劳动密集型的手动分析方法既不可行也不可扩展。因此，机器学习被提出来作为一种解决方案，但是训练相应的模型仍然需要大量的手动注释。在这里，我们提出了一种基于图像的自动化工作流程，用于使用更快的 R-CNN 进行大型底栖动物检测（FaunD-Fast）。该工作流程通过自动化检测水下图像中具有相对于海底背景异常属性的异常超像素，显著减少了所需的注释工作量。所检测到的异常超像素的边界框坐标被提议作为一组弱注释，然后为其分配语义形态类型标签，并用于训练更快的 R-CNN 对象检测模型。我们将此工作流程应用于在克拉里昂-克利珀顿区（CCZ）德国和比利时锰结核勘探区 SO268 航次记录的示例水下图像。我们的 FaunD-Fast 模型的性能评估显示，在交并比阈值为 0.5 时，平均精度为 78.1%，与使用昂贵注释的竞争模型相当。更详细地，对大型动物检测结果的分析表明，蛇尾类和异足类是最丰富的形态类型之一，在调查区域内的所有检测中占 62%。进一步研究两个合同区之间的区域差异表明，在较浅的德国区域，大型动物的丰度和多样性都更高，这可能是由于在 CCZ 从东向西下降的沉降有机物质形式提供了更高的食物供应。由于这些发现与基于传统图像方法的研究一致，我们得出结论，我们的自动化工作流程显著减少了所需的人工工作量，同时仍然提供了大型动物丰度及其空间分布的准确估计。因此，该工作流程可用于快速但客观地生成基线信息，以实现对远程底栖生态系统的监测。
