Research on the wave field evolution law is highly significant to the fields of offshore engineering and marine resource development.Numerical simulations have been conducted for high-precision wave field evolution,th...Research on the wave field evolution law is highly significant to the fields of offshore engineering and marine resource development.Numerical simulations have been conducted for high-precision wave field evolution,thus providing short-term wave field prediction.However,its evolution occurs over a long period of time,and its accuracy is difficult to improve.In recent years,the use of machine learning methods to study the evolution of wave field has received increasing attention from researchers.This paper proposes a wave field evolution method based on deep convolutional neural networks.This method can effectively correlate the spa-tiotemporal characteristics of wave data via convolution operation and directly obtain the offshore forecast results of the Bohai Sea and the Yellow Sea.The attention mechanism,multi-scale path design,and hard example mining training strategy are introduced to suppress the interference caused by Weibull distributed wave field data and improve the accuracy of the proposed wave field evolu-tion.The 72-and 480-h evolution experiment results in the Bohai Sea and the Yellow Sea show that the proposed method in this pa-per has excellent forecast accuracy and timeliness.展开更多
A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can...A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can also detect the TV logo from photo pictures taken by smartphones or other smart terminals. Firstly, using a simple and effective way of collecting and labelling TV logo, a large-scale TV logo dataset used to train the detection model is built. Then, parameters and loss function of SSD are modified to make it more suitable for the task of TV logo detection. Moreover, a soft-NMS algorithm is introduced to remove the redundant overlapping boxes and obtain the final output box. And also an approach for hard example mining is designed to improve the detection accuracy. Finally, extensive comparison experiments are carried out which take into consideration different image resolutions, logo positions and environmental factors existing in real-world applications. Experimental results demonstrate that the proposed method achieve superior performances in robustness compared to other state-of-the-art methods.展开更多
为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利...为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利用高斯降权函数对其置信度进行衰减,通过判别参数对衰减后的置信度作进一步判断;在训练过程中加入采用优化NMS算法的在线难例挖掘(Online hard example mining,OHEM)技术,对马铃薯芽眼进行识别试验。试验结果表明:改进的模型识别精度为96.32%,召回率为90.85%,F1为93.51%,平均单幅图像的识别时间为0.183 s。与原始的Faster R-CNN模型相比,改进的模型在不增加运行时间的前提下,精度、召回率、F1分别提升了4.65、6.76、5.79个百分点。改进Faster R-CNN模型能够实现马铃薯芽眼的有效识别,满足实时处理的要求,可为种薯自动切块中的芽眼识别提供参考。展开更多
基金supported by the National Key Research and Development Project(No.2018YFC1407001).
文摘Research on the wave field evolution law is highly significant to the fields of offshore engineering and marine resource development.Numerical simulations have been conducted for high-precision wave field evolution,thus providing short-term wave field prediction.However,its evolution occurs over a long period of time,and its accuracy is difficult to improve.In recent years,the use of machine learning methods to study the evolution of wave field has received increasing attention from researchers.This paper proposes a wave field evolution method based on deep convolutional neural networks.This method can effectively correlate the spa-tiotemporal characteristics of wave data via convolution operation and directly obtain the offshore forecast results of the Bohai Sea and the Yellow Sea.The attention mechanism,multi-scale path design,and hard example mining training strategy are introduced to suppress the interference caused by Weibull distributed wave field data and improve the accuracy of the proposed wave field evolu-tion.The 72-and 480-h evolution experiment results in the Bohai Sea and the Yellow Sea show that the proposed method in this pa-per has excellent forecast accuracy and timeliness.
基金Supported by the National Natural Science Foundationof China(No.61702466)“Double Tops” Discipline Construction Project
文摘A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can also detect the TV logo from photo pictures taken by smartphones or other smart terminals. Firstly, using a simple and effective way of collecting and labelling TV logo, a large-scale TV logo dataset used to train the detection model is built. Then, parameters and loss function of SSD are modified to make it more suitable for the task of TV logo detection. Moreover, a soft-NMS algorithm is introduced to remove the redundant overlapping boxes and obtain the final output box. And also an approach for hard example mining is designed to improve the detection accuracy. Finally, extensive comparison experiments are carried out which take into consideration different image resolutions, logo positions and environmental factors existing in real-world applications. Experimental results demonstrate that the proposed method achieve superior performances in robustness compared to other state-of-the-art methods.
文摘为提高对马铃薯芽眼的识别效果,提出一种基于改进Faster R-CNN的马铃薯芽眼识别方法。对Faster RCNN中的非极大值抑制(Non-maximum suppression,NMS)算法进行优化,对与M交并比(Intersection over union,IOU)大于等于Nt的相邻检测框,利用高斯降权函数对其置信度进行衰减,通过判别参数对衰减后的置信度作进一步判断;在训练过程中加入采用优化NMS算法的在线难例挖掘(Online hard example mining,OHEM)技术,对马铃薯芽眼进行识别试验。试验结果表明:改进的模型识别精度为96.32%,召回率为90.85%,F1为93.51%,平均单幅图像的识别时间为0.183 s。与原始的Faster R-CNN模型相比,改进的模型在不增加运行时间的前提下,精度、召回率、F1分别提升了4.65、6.76、5.79个百分点。改进Faster R-CNN模型能够实现马铃薯芽眼的有效识别,满足实时处理的要求,可为种薯自动切块中的芽眼识别提供参考。
