[1]杜艳玲,王丽丽,黄冬梅,等.融合密集特征金字塔的改进R2CNN海洋涡旋自动检测[J].智能系统学报,2023,18(2):341-351.[doi:10.11992/tis.202112019]
DU Yanling,WANG Lili,HUANG Dongmei,et al.Improved R2CNN ocean eddy automatic detection with a dense feature pyramid[J].CAAI Transactions on Intelligent Systems,2023,18(2):341-351.[doi:10.11992/tis.202112019]
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DU Yanling,WANG Lili,HUANG Dongmei,et al.Improved R2CNN ocean eddy automatic detection with a dense feature pyramid[J].CAAI Transactions on Intelligent Systems,2023,18(2):341-351.[doi:10.11992/tis.202112019]
融合密集特征金字塔的改进R2CNN海洋涡旋自动检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第2期
页码:
341-351
栏目:
学术论文—机器感知与模式识别
出版日期:
2023-05-05
- Title:
- Improved R2CNN ocean eddy automatic detection with a dense feature pyramid
- Keywords:
- deep learning; object detection; ocean eddy; dense feature pyramid network; convolution neural network; feature fusion; R2CNN; pattern recognition
- 分类号:
- TP183; TP391
- 摘要:
- 海洋涡旋演变过程认识的不足是制约当前物理海洋研究水平的关键因素，海洋涡旋自动检测是掌握其产生、发展、变异过程机理及其与多尺度海洋过程相互作用的基础。然而，由于海洋涡旋尺度多样性、形状不规则、分布密集的特点，现有水平检测方法导致检测区域存在显著的冗余、重叠与嵌套。为解决上述问题，提出多尺度旋转密集特征金字塔网络。具体地，通过密集连接(dense feature pyramid network, DFPN)改进特征金字塔网络实现多尺度高层语义特征提取与融合，增强特征传播与特征重用；此外，针对海洋涡旋密集分布的特点，改进旋转区域卷积神经网络(rotational region convolutional neural network, R2CNN)，提出多尺度RoI Align机制，实现特征的语义保持和空间信息的完整性，提升模型检测性能。最后，采用海平面异常值数据构建海洋涡旋数据集，并预处理成VOC格式进行训练，调整相应参数得到检测模型。实验结果表明，提出的检测模型最优检测精度可达96.4%，并对太平洋、大西洋海域的海洋涡旋进行自动检测，验证了模型具有较好的泛化能力。
- Abstract:
- The lack of understanding of the evolution process of ocean eddies is the main obstacle to current physical ocean research. To gain a better understanding of the mechanism of its generation, development, and variation process, as well as its interaction with multi-scale ocean processes, the automatic detection of ocean eddies is crucial. However, existing horizontal detection methods struggle to accurately detect ocean eddies due to their scale diversity, irregular shape, and dense distribution, leading to significant redundancy and overlap in the detection area. To address this issue, we propose a multi-scale rotation-dense feature pyramid network. The feature pyramid network is enhanced through dense connection (DFPN) for multi-scale high-level semantic feature extraction and fusion, improving feature propagation and reuse. To accommodate the dense distribution of ocean eddies, the R2CNN network is improved, and a multi-scale RoI Align mechanism is introduced to preserve feature semantics and spatial information integrity, thus improving model detection performance. We construct the ocean eddy dataset using sea-level anomaly data, which is preprocessed into VOC format for training. The parameters are adjusted to obtain the optimal detection model, and experimental results show that the model’s detection accuracy can reach 96.4%. The proposed model demonstrates good generalisability through the automatic detection of ocean eddies in the Pacific and Atlantic Oceans.
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备注/Memo
收稿日期:2021-12-10。
基金项目:国家自然科学基金项目（41906179）；上海市科委地方能力建设项目(20050501900).
作者简介:杜艳玲,讲师,主要研究方向为海洋中尺度涡自动检测与轨迹追踪。主持国家自然科学基金项目1项、中国博士后基金项目1项,以项目骨干参与省部级以上项目10余项。获"领跑者5000中国精品科技期刊顶尖学术论文",上海市科学技术奖等省部级以上奖励4项,申请专利9项。在国际顶级期刊和CCF推荐国际会议上发表学术论文5篇;王丽丽,硕士研究生,主要研究方向为目标检测、深度学习;贺琪,教授,上海海洋大学信息学院副院长,中国计算机学会(CCF)上海委员,中国海洋学会海洋信息专业委员会委员,主要研究方向为海洋大数据存储与分析、遥感影像分类与目标识别。曾获得上海市教学成果奖二等奖、上海海洋科学技术奖一等奖、上海市浦东新区科技进步一等奖等奖项。出版专著《海洋大数据》、《海洋信息技术与应用》等。发表学术论文40余篇。
通讯作者:贺琪. E-mail：qhe@shou.edu.cn
更新日期/Last Update:
1900-01-01