Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the...Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.展开更多
针对目前人工识别羊个体疼痛过程中存在的经验要求高、识别准确率低、消耗成本高、延误疾病治疗等问题,引入当前主流图像分类网络VGGNet(Visual geometry group network)对有疼痛和无疼痛的羊脸表情进行识别,提出一种基于改进VGGNet的...针对目前人工识别羊个体疼痛过程中存在的经验要求高、识别准确率低、消耗成本高、延误疾病治疗等问题,引入当前主流图像分类网络VGGNet(Visual geometry group network)对有疼痛和无疼痛的羊脸表情进行识别,提出一种基于改进VGGNet的羊脸痛苦表情识别算法,改进后的网络为STVGGNet(Spatial transformer visual geometry group network)。该算法将空间变换网络引入VGGNet,通过空间变换网络增强对羊脸痛苦表情特征区域的关注程度,提高对羊脸痛苦表情的识别准确率。本文对原有的羊脸表情数据集进行了扩充,新增887幅羊脸表情图像。但是新的数据集图像数量仍然较少,所以本文利用ImageNet数据集进行迁移学习,微调后用来自动分类有痛苦和无痛苦的羊脸表情。对羊面部表情数据集的实验结果表明,使用STVGGNet实现的最佳训练准确率为99.95%,最佳验证准确率为96.06%,分别比VGGNet高0.15、0.99个百分点。因此,本文采用的模型在羊脸痛苦表情识别中有非常好的识别效果并且具有较强的鲁棒性,为畜牧业中羊的疾病检测智能化发展提供了技术支撑。展开更多
基金The National Key R&D Program of China under contract Nos 2022YFC3003800,2020YFC1521700 and 2020YFC1521705the National Natural Science Foundation of China under contract No.41830540+3 种基金the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No.OR-SECCZ2022104the Deep Blue Project of Shanghai Jiao Tong University under contract No.SL2020ZD204the Special Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research Institutes of Public Interest of China under contract No.SZ2102the Zhejiang Provincial Project under contract No.330000210130313013006。
文摘Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.
文摘针对目前人工识别羊个体疼痛过程中存在的经验要求高、识别准确率低、消耗成本高、延误疾病治疗等问题,引入当前主流图像分类网络VGGNet(Visual geometry group network)对有疼痛和无疼痛的羊脸表情进行识别,提出一种基于改进VGGNet的羊脸痛苦表情识别算法,改进后的网络为STVGGNet(Spatial transformer visual geometry group network)。该算法将空间变换网络引入VGGNet,通过空间变换网络增强对羊脸痛苦表情特征区域的关注程度,提高对羊脸痛苦表情的识别准确率。本文对原有的羊脸表情数据集进行了扩充,新增887幅羊脸表情图像。但是新的数据集图像数量仍然较少,所以本文利用ImageNet数据集进行迁移学习,微调后用来自动分类有痛苦和无痛苦的羊脸表情。对羊面部表情数据集的实验结果表明,使用STVGGNet实现的最佳训练准确率为99.95%,最佳验证准确率为96.06%,分别比VGGNet高0.15、0.99个百分点。因此,本文采用的模型在羊脸痛苦表情识别中有非常好的识别效果并且具有较强的鲁棒性,为畜牧业中羊的疾病检测智能化发展提供了技术支撑。
