MDNet: Multi-Patch Dense Network for Coral Classification
MDNet: Multi-Patch Dense Network for Coral Classification
复制标题
MDNet：用于珊瑚分类的多补丁密集网络
DOI：
10.1109/oceans.2018.8604478
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Ioannis M. Rekleitis
中科院分区：
文献类型：
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作者：
M. Modasshir;Alberto Quattrini Li;Ioannis M. Rekleitis
从视觉数据中分类珊瑚物种是一项具有挑战性的任务，因为物种内的差异很大，物种间的相似性很高，水下图像清晰度不一致，数据集不平衡。此外，海洋生物学家对珊瑚礁图像使用的标注方法——点标注，也容易出现标注错误。点标注还使现有的数据集与使用边界框标注技术的最新分类方法不兼容。在本文中，我们提出了一种新颖的端到端卷积神经网络（CNN）架构，Multi-Patch Dense Network (MDNet)，它可以从点注释的视觉数据中学习分类珊瑚物种。该方法利用以点标注对象为中心的不同尺度的补丁。此外，MDNet利用层之间的密集连接来减少对不平衡数据集的过度拟合。给出了在Moorea Labeled Coral （MLC）基准数据集上的实验结果。所提出的MDNet比最先进的方法具有更高的精度和平均类精度。
Classifying coral species from visual data is a challenging task due to significant intra-species variation, high interspecies similarity, inconsistent underwater image clarity, and high dataset imbalance. In addition, point annotation, the labeling method used for coral reef images by marine biologists, is prone to mislabeling. Point annotation also makes existing datasets incompatible with state-of-the-art classification methods which use the bounding box annotation technique. In this paper, we present a novel end-to-end Convolutional Neural Network (CNN) architecture, Multi-Patch Dense Network (MDNet) that can learn to classify coral species from point annotated visual data. The proposed approach utilizes patches of different scale centered on point annotated objects. Furthermore, MDNet utilizes dense connectivity among layers to reduce over-fitting on imbalanced datasets. Experimental results on the Moorea Labeled Coral (MLC) benchmark dataset are presented. The proposed MDNet achieves higher accuracy and average class precision than the state-of-the-art approaches.