A Sea Creatures Classification Method using Convolutional Neural Networks
A Sea Creatures Classification Method using Convolutional Neural Networks
复制标题
DOI：
--
复制
发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Jonghyun Ahn;Yuya Nishida;K. Ishii;T. Ura
中科院分区：
文献类型：
--
作者：
Jonghyun Ahn;Yuya Nishida;K. Ishii;T. Ura
近年来，自主水下航行器（AUV）为研究人员提供了高分辨率的海底图像。提供的图像由研究人员逐一分类，这项任务增加了研究人员的负担。在这项研究中，我们提出了一个自动分类方法的海洋生物使用卷积神经网络提供所需的信息，如螃蟹的图像，研究人员。该方法包括图像增强过程、分割过程和分类过程。在图像增强过程中，采用Retinex模型，提高了海底图像的可见度。对于候选区域的选择，显著性图被用来提取海底图像中的代表区域。在分类过程中，基于生物组识别所选择的区域。总候选检测率为64%，总识别准确率为67%。
Recently, Autonomous Underwater Vehicles (AUVs) provide high resolution seafloor images to the researchers. The provided images are classified by the researchers one by one, and this task increases the burden on the researchers. In this research, we propose an automatic classification method for the sea creatures using convolutional neural networks to provide the requested information, such as crab images, to researcher. The proposed method is comprised image enhancement process, segmentation process and classification process. In the image enhancement process, which is used Retinex model, the visibility of seafloor images is improved. For the candidate's area selection, the Saliency map is employed to extract the represented areas in seafloor images. In the classification process, the selected areas are recognized based on the biological group. The total candidate detection rate was 64%, and the total recognition accuracy was 67% by the evaluation.