Objective

2

Nearshore waves are significantly affected by seabed topography

shore

and environmental flows

following complex evolutionary laws with faster temporal and spatial transformations than open sea waves. Therefore

measuring nearshore wave height is significant for nearshore engineering design

shallow sea production operations

and nearshore environmental protection. The traditional wave height measure mainly relies on wave buoy monitoring. Compared with the traditional manner

nearshore video surveillance has advantages in uninterrupted data acquisition and abundant visual expression of waves. However

automatic wave height detection of nearshore waves through videos is insufficient at present. Existing methods of wave height detection based on visual information can be divided into two categories:1) Wave parameter detection based on stereo vision. Most models are complex

lacking robustness

and characterized by unstable detection of wave height; thus

they cannot satisfy the practical application. 2) Wave parameter detection based on image/video extracted features

including statistical features

transform domain features

and texture features. This type of method is mainly used to detect the direction of wave and wave height

which necessitates design features in advance

and is thereby limited by prior knowledge. In recent years

deep learning has achieved considerable success in image identification

nature language processing

and object recognition. Combined with the feature learning mechanism of deep learning

an automatic wave height detection method for nearshore wave video is proposed in this paper.

Method

2

The proposed method mainly involves data preprocessing

model design and feature fusion

and regression prediction. First

the video frames are extracted from the nearshore surveillance video at intervals

and the two adjacent frames are subtracted to form a set of differential frames. The dataset of original video frames contains static spatial information of waves and the dataset of differential images contains motion information of waves. To avoid the influence of reefs and buildings on wave feature extraction

we intercepted the wave area in the video by eliminating near-zero parts in the differential image. To enhance the generalization ability and robustness of the model

we used the data augment method to rotate and stretch the image to increase the number and diversity of datasets. Second

a network in networks (NIN)-based system for wave height detection was constructed. The high-level spatial and temporal features are learned by two independent NINs using static and differential images of the waves as input. The 4-layer structure of NIN is used for the spatial feature learning

while the 2-layer structure is used for the temporal feature learning. The two types of features are fused by simple concatenation because pixel-wise fusion may bring mutual interference and information loss. Finally

the fused features are fed into a support vector regression (SVR) model that maps features into 1D space and performs regression to achieve automatic detection of wave height of nearshore video images.

Result

2

Our wave videos were collected from a marine station in China Sea from November 2015 to November 2016. The shooting time ranged from 7 a.m. to 4 p.m. To explore the performance of the different network models and the effect of the sample size of dataset on the wave height detection results

we conducted two sets of experiments. Experiment 1:We compared our NIN-based wave height detection model with a classic 2-layer convolutional neural network and a more advanced dense convolutional network (DenseNet). Based on the root-mean-square-error (RMSE) between predicted wave heights and the ground truth as the assessment index

the comparison results show that NIN-based network model can achieve more accurate wave height prediction with RMSE of 0.188 4. Experiment 2:To select the appropriate network input size

the wave height detection models with different sample sizes were trained and their performance was compared on test datasets under different tolerant ranges of absolute error from 0.2-0.4. The result shows that the input sample size of 32×32 pixels has the highest accuracy under the condition of absolute error < 0.2 and has a relatively stable change as the change of absolute error ranges. In consideration of the integrity of image feature information expression and noise interference

the experimental data used in this study was set to the uniform size of 32×32 pixels. Experiment 3:The roles of temporal and spatial feature fusions were examined. The high-level spatial features were learned from static video frames and the temporal features from the difference image between two adjacent frames. Compared with only using spatial features

fusing spatial features with temporal features achieved a significant increase in wave detection accuracy under various tolerance ranges of absolute error

and the detected wave height is less fluctuating. The average absolute error of the method in the detection of wave height is 0.109 5 m

and the average relative error is 7.39%. According to the wave height levels

the wave height detected by our proposed method can satisfy the absolute error range of±0.1 m below the wave level 2. The average relative error of the wave height above level 2 is less than 20%

which satisfies the demand for operational use of wave forecasting.

Conclusion

2

The method can be used to automatically obtain wave height value from nearshore wave videos

which effectively compensates for the incompleteness of artificial design features. Moreover

our method has better practicality within the scope of operational detection requirements; thus

it has an average relative error of wave height ≤ 20%. Our method can meet the requirements for accuracy and efficiency in significant wave height detection

and provides a new platform to use nearshore videos for wave monitoring.