CN110929794A - Side-scan sonar image classification method based on multi-task learning - Google Patents
Side-scan sonar image classification method based on multi-task learning Download PDFInfo
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Abstract
The invention belongs to the technical field of side-scan sonar image identification, and particularly relates to a side-scan sonar image classification method based on multi-task learning. The invention combines the multi-task learning idea and the convolutional neural network method, uses the convolutional neural network to automatically extract the features, can extract important features which cannot be sensed by human eyes compared with the traditional manually-arranged feature extractor, and can also reduce the influence of factors such as side scan sonar image noise, image edge loss, image feature deformation and the like on the feature extraction. According to the method, the idea of multi-task learning is utilized, and an optical image classification task is introduced, so that the feature space of the side scan sonar image can be enriched, and the problem of overfitting caused by incomplete feature extraction when the number of samples is too small is solved; the method can solve the problems of few side-scan sonar image samples and poor classification effect when the feature extraction is difficult, and has certain engineering and research values.
Description
Technical Field
The invention belongs to the technical field of side-scan sonar image identification, and particularly relates to a side-scan sonar image classification method based on multi-task learning.
Background
Compared with an optical imaging system, the side-scan sonar system can overcome the limitation of severe conditions in water and acquire images of the sea bottom in a long distance. The sonar image has the application which is difficult to be compared with other tools in the fields of submarine surveying and mapping, submarine geological investigation, deep water battle, submarine rescue and the like. However, due to the vast sea, it is very difficult to acquire enough side scan sonar images containing the target to be searched. On the other hand, because the quality of the side-scan sonar image may be affected by illumination, temperature, sand, stone and the like, and the target characteristics in the side-scan sonar image are far from the real target characteristics due to the factors, a good target classification effect is difficult to obtain only by using the limited side-scan sonar image.
At present, the classification of side-scan sonar images is mainly based on a traditional machine learning method, a feature extractor is designed firstly, and then a proper classifier is selected for classification. The most important of them is feature extraction, which directly determines the upper bound of classification accuracy. However, the imaging principle of the side scan sonar causes the problems of more noise points, obvious shadow and incomplete edge of the spliced side scan sonar images. This results in that conventional point and line feature extraction methods such as Frstner, Harris and houghtorsform do not extract discriminative features well. The popular deep learning method based on the convolutional neural network achieves good effects in the aspects of image recognition and classification, even exceeds the level of human beings, however, the performances are based on strong computing power, a large number of samples and efficient algorithms. For side scan sonar images, due to the vast sea, scenes containing objects (such as airplanes, ships, etc.) are not many, and it is very difficult to scan the objects, which directly results in very few samples of the side scan sonar images. In this case, the effect achieved by the deep learning may not even be comparable to the conventional method that has been elaborated.
In summary, side scan sonars are widely used in ocean exploration, but due to the reasons of small sample size and poor quality of side scan sonar image targets, the current target identification method does not achieve good effect in side scan sonar image classification.
Disclosure of Invention
The invention aims to provide a side-scan sonar image classification method based on multi-task learning, which is used for finishing the training and classification of a side-scan sonar image classification network under the condition of small number of side-scan sonar image samples.
The purpose of the invention is realized by the following technical scheme: the method comprises the following steps:
step 1: constructing a classified side-scan sonar image data set according to the existing side-scan sonar data;
step 2: constructing an optical image auxiliary data set related to the target according to the target category in the side scan sonar data set;
and step 3: constructing a multi-task learning model based on a convolutional neural network; the multi-task learning model based on the convolutional neural network has two inputs which share a convolutional pooling layer; each input has a respective classification layer and calculates its own loss function independently; the total loss function of the multi-task learning model based on the convolutional neural network is the sum of the weighted loss functions of two inputs;
and 4, step 4: taking data in the side-scan sonar image data set and the optical image auxiliary data set as input of a multi-task learning model based on a convolutional neural network, setting network parameters and training the multi-task learning model based on the convolutional neural network to obtain an automatic classification network of the side-scan sonar image target;
and 5: verifying the automatic classification network performance of the side-scan sonar image target by using a verification set, and recording the highest accuracy; if the highest accuracy rate does not meet the requirement, adjusting the weight ratio of the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network, and returning to the step 4 for retraining;
step 6: and inputting the image detected by the side-scan sonar in real time into an automatic classification network of the side-scan sonar image target to obtain a classification result.
The present invention may further comprise:
in the step 3, a multitask learning model based on a convolutional neural network is constructed, the convolutional neural network is used for feature extraction, the convolutional neural network is constructed by imitating a biological visual mechanism, and the convolutional kernel sharing of the middle layer and the sparsity of interlayer connection enable the convolutional neural network to automatically and stably learn the image features with smaller calculated amount and have no additional feature engineering;
the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network in the step 3 are specifically as follows:
the penalty function for the optical image auxiliary dataset classification task is:
wherein M is the number of auxiliary task samples in the optical image auxiliary data set participating in training; (x) is the fitting function of the convolved, pooled layers; g1(x) Fitting function of full connection layer for auxiliary task; x is the number ofs (i)The ith sample participating in training in the optical image auxiliary data set; y iss (i)A label corresponding to the ith sample in the optical image auxiliary data set;
the loss function for the side scan sonar image dataset classification task is:
wherein N is the number of target task samples in the side scan sonar image data set participating in training; (x) is the fitting function of the convolved, pooled layers; g2(x) A fitting function of a fully connected layer for the target task; x is the number oft (i)The ith sample participating in training is collected for the side scan sonar image data; y ist (i)Is a sideScanning a label corresponding to the ith sample in the sonar image data set;
the final set total loss function of the convolutional neural network-based multitask learning model is:
J＝α*Js+β*Jt
α represents the weight of the auxiliary task and the target task, and is initialized to 1: 1.
The invention has the beneficial effects that:
the invention combines the multi-task learning idea and the convolutional neural network method, uses the convolutional neural network to automatically extract the features, can extract important features which cannot be sensed by human eyes compared with the traditional manually-arranged feature extractor, and can also reduce the influence of factors such as side scan sonar image noise, image edge loss, image feature deformation and the like on the feature extraction. According to the method, the idea of multi-task learning is utilized, and an optical image classification task is introduced, so that the feature space of the side scan sonar image can be enriched, and the problem of overfitting caused by incomplete feature extraction when the number of samples is too small is solved; the method can solve the problems of few side-scan sonar image samples and poor classification effect when the feature extraction is difficult, and has certain engineering and research values.
Drawings
FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is an image of a side scan sonar in the side scan sonar image data set.
Fig. 3 is an optical image in an optical image auxiliary data set.
Fig. 4 is a diagram of a network architecture of the present invention.
FIG. 5(a) is a graph of the loss function of the network training process at a 1:1 weight ratio.
FIG. 5(b) is a graph of the accuracy of the network training process at a 1:1 weight ratio.
FIG. 6 is a comparison table of prediction accuracy under different weighting ratios according to the present invention.
FIG. 7 is a table comparing the side scan sonar image prediction results of the present invention with other methods.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention belongs to the field of side scan sonar image identification, and particularly relates to a side scan sonar image classification method based on multi-task learning (MTL-CNN); aiming at the limitation of the current side-scan sonar image classification method, the invention aims to provide a side-scan sonar image classification method based on multi-task learning, and the invention aims to finish the training and classification of a side-scan sonar image classification network end to end by using a deep learning method under the condition that the number of side-scan sonar image samples is small.
The invention finds out the side scan sonar image containing the target from the existing side scan sonar image; finding out an optical image which is labeled and has the same target as that in the side scan sonar image from the existing ImageNet data set; and then a multitask learning model based on a convolutional neural network is built, the network is trained by combining an optical data set and a side-scan sonar data set, and the classification capability of the deep learning network on the side-scan sonar target can be improved by training the network by combining the optical data set. Considering that the features of the side-scan sonar images are difficult to extract by using a traditional method, the conventional deep learning target identification and classification method can extract features with self-adaptability, but a large number of samples are needed, and the real samples of the side-scan sonar images are high in acquisition cost and difficulty and difficult to meet the requirements of the deep learning method. Therefore, the side-scan sonar image classification method based on multi-task learning can solve the problem that side-scan sonar image samples are few, and therefore side-scan sonar image targets can be well classified.
A side scan sonar image classification method based on multitask learning comprises the following steps:
step 1: constructing a classified side-scan sonar image data set according to the existing side-scan sonar data;
step 2: constructing an optical image auxiliary data set related to the target according to the target category in the side scan sonar data set;
and step 3: constructing a multi-task learning model based on a convolutional neural network; the multi-task learning model based on the convolutional neural network has two inputs which share a convolutional pooling layer; each input has a respective classification layer and calculates its own loss function independently; the total loss function of the multi-task learning model based on the convolutional neural network is the sum of the weighted loss functions of two inputs;
and 4, step 4: taking data in the side-scan sonar image data set and the optical image auxiliary data set as input of a multi-task learning model based on a convolutional neural network, setting network parameters and training the multi-task learning model based on the convolutional neural network to obtain an automatic classification network of the side-scan sonar image target;
and 5: verifying the automatic classification network performance of the side-scan sonar image target by using a verification set, and recording the highest accuracy; if the highest accuracy rate does not meet the requirement, adjusting the weight ratio of the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network, and returning to the step 4 for retraining;
step 6: and inputting the image detected by the side-scan sonar in real time into an automatic classification network of the side-scan sonar image target to obtain a classification result.
3, constructing a multitask learning model based on a convolutional neural network, extracting features by using the convolutional neural network, constructing the convolutional neural network by simulating a biological visual mechanism, wherein the convolutional kernel of the middle layer is shared and the sparsity of interlayer connection enables the convolutional neural network to automatically and stably learn the image features with a small calculated amount and no additional feature engineering exists;
the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network in the step 3 are specifically as follows:
the penalty function for the optical image auxiliary dataset classification task is:
wherein M is the number of auxiliary task samples in the optical image auxiliary data set participating in training; (x) is the fitting function of the convolved, pooled layers; g1(x) Fitting function of full connection layer for auxiliary task; x is the number ofs (i)The ith sample participating in training in the optical image auxiliary data set; y iss (i)A label corresponding to the ith sample in the optical image auxiliary data set;
the loss function for the side scan sonar image dataset classification task is:
wherein N is the number of target task samples in the side scan sonar image data set participating in training; (x) is the fitting function of the convolved, pooled layers; g2(x) A fitting function of a fully connected layer for the target task; x is the number oft (i)The ith sample participating in training is collected for the side scan sonar image data; y ist (i)A label corresponding to the ith sample in the side scan sonar image data set;
the final set total loss function of the convolutional neural network-based multitask learning model is:
J＝α*Js+β*Jt
α represents the weight of the auxiliary task and the target task, and is initialized to 1: 1.
Example 1:
a side scan sonar image classification method based on multitask learning specifically comprises the following steps:
step 1: constructing a side scan sonar image data set, and classifying the existing side scan sonar data set, wherein three types of airplanes, ships and other objects except the airplanes and the ships are taken as examples for explanation;
step 2: acquiring images of the airplane and the ship under an optical scene similar to the images of the airplane and the ship in the side scan sonar image data set from the existing data set;
and step 3: and constructing a multi-task learning model based on the convolutional neural network, wherein the model is modified on the basis of the VGG11 convolutional neural network model. The model has two inputs and shares the convolution pooling layer. Each input has a respective classification level (fully connected level) and its own loss function is computed separately. The total loss function of the multitask network model is the sum of the weighted loss functions of the two inputs, and the weight ratio is initialized (generally 1: 1);
and 4, step 4: using the two data sets in the steps 1 and 2 as the input of the network model in the step 3, then training the network model, and recording the highest accuracy of the model on the side scan sonar image verification set;
and 5: if the accuracy is at the maximum (or close to convergence), it is available, and then the process is finished; otherwise, the weight ratio is adjusted, and the step 4 is returned to.
And 2, acquiring the images of the airplane and the ship under the optical scene similar to the images of the airplane and the ship in the side scan sonar image data set from the existing data set, wherein the data set can assist the side scan sonar data set to train and solve the problem of insufficient data of the side scan sonar data set.
And 3, building a multi-task learning model based on the convolutional neural network, and extracting features by using the convolutional neural network. The convolutional neural network is constructed by imitating the visual mechanism of a living being, and the convolution kernel sharing of the middle layer and the sparsity of connection between the layers enable the convolutional neural network to automatically and stably learn the image characteristics with small calculation amount and no additional characteristic engineering.
And 3, building a multi-task learning model based on the convolutional neural network. The model combines the multi-task learning idea and the convolutional neural network in the claim 3, is modified on the basis of VGG11, and is added with an input and an output. The two inputs to the model are a side-scan sonar image and an optical image, respectively, which share a convolutional pooling layer and have separate classification layers. By sharing the convolution pooling layer, the bottom-layer characteristics of the optical image with sufficient data volume can be fully utilized by the side-scan sonar image classification task, and the problem of insufficient side-scan sonar image characteristic extraction is solved; the total loss function of the multitask network is the sum of two input weighted loss functions, and by simultaneously optimizing the loss functions of the optical image and the side-scan sonar image classification, the optical image classification task can extract more features, and the features can be better applied to the classification task of the side-scan sonar image.
In the step 5, the optimal proportion is found by adjusting the weight ratio of the loss functions of the auxiliary task (optical image target classification network training) and the target task (side scan sonar image target classification network training), so that the classification effect of the whole network is optimal, the classification of the side scan sonar images is facilitated, and the network generalization performance is stronger.
Compared with the prior art, the invention has the advantages that: combines the multi-task learning idea and the convolution neural network method. The convolutional neural network is used for automatic feature extraction, and compared with a traditional manually-arranged feature extractor, the convolutional neural network can extract important features which cannot be sensed by human eyes, and can also reduce the influence of factors such as side-scan sonar image noise, image edge loss, image feature deformation and the like on feature extraction; by utilizing the idea of multi-task learning and introducing an optical image classification task, the feature space of the side scan sonar image can be enriched, and the problem of overfitting caused by incomplete feature extraction when the number of samples is too small is avoided; the method can solve the problems of few side-scan sonar image samples and poor classification effect when the feature extraction is difficult, and has certain engineering and research values.
The invention will be further described by way of practical examples, without in any way limiting the scope of the invention, with reference to the accompanying drawings.
As shown in fig. 1, is a flow chart of the method of the present invention, which specifically comprises the following steps:
step 1: constructing a side scan sonar image data set, and dividing the side scan sonar image data set into three types, namely an airplane, a ship and other targets, as shown in an attached figure 2;
step 2: constructing an optical image data set which is divided into two types, namely an airplane and a ship, as shown in figure 3;
and step 3: a convolutional neural network based multi-task learning (MTL-CNN) model was constructed as shown in fig. 4. The model has two inputs, a side scan sonar image and an optical image. The two inputs share the convolutional pooling layer of the convolutional neural network and have independent fully-connected layers. The penalty function for the ImageNet dataset (helper dataset) classification task is:
where M represents the number of auxiliary samples involved in the training, f (x) represents the fitting function of the convolved, pooled layers, g1(x) Fitting function, x, of the fully connected layer representing the auxiliary tasks (i)Representing the i-th sample involved in the training, ys (i)Representing the label corresponding to the ith sample.
The penalty function for the side scan sonar dataset (target dataset) classification task is:
where N represents the number of target task samples involved in the training, f (x) represents the fitting function of the convolution, pooling layers, g2(x) Fitting function, x, of the fully connected layer representing the auxiliary taskt (i)Representing the i-th sample involved in the training, yt (i)Representing the label corresponding to the ith sample.
The optimized loss function for the final setting is:
J＝α*Js+β*Jt
α respectively represents the weights of the auxiliary task and the target task, and is generally initialized to 1: 1;
step 4, using the two data sets in the steps 1 and 2 as the input of the network model in the step 3, wherein the number of the plane images in the side scan sonar image data set is 120, the number of the ship is 160, and the number of the other images is 250, the number of the plane images and the ship images in the optical image data set are 1200, the initialization α: β is 1:1, other parameters use default values, then the network model is trained, and the highest accuracy of the model on the side scan sonar image test set is recorded, and as shown in the attached figure 5(a) and the figure 5(b), the loss function and the accuracy chart of the network training process under the weight ratio of 1:1 are respectively shown;
and 5: if the accuracy is at the maximum (or close to convergence), ending; otherwise, the weight ratio is adjusted, and the step 4 is returned to. After several experiments, the optimal weight ratio of the auxiliary task to the target task is determined to be 1: 10. Fig. 6 shows the accuracy (accuracy), Precision (Precision), and Recall (Recall) of the network trained under different specific gravities on the side scan sonar image. In addition, compared with other methods such as a common Convolutional Neural Network (CNN) method and a Fine-tuning (Fine-tuning) method, the effectiveness of the convolutional neural network-based multitask learning method is proved, as shown in fig. 7.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. A side scan sonar image classification method based on multitask learning is characterized by comprising the following steps:
step 1: constructing a classified side-scan sonar image data set according to the existing side-scan sonar data;
step 2: constructing an optical image auxiliary data set related to the target according to the target category in the side scan sonar data set;
and step 3: constructing a multi-task learning model based on a convolutional neural network; the multi-task learning model based on the convolutional neural network has two inputs which share a convolutional pooling layer; each input has a respective classification layer and calculates its own loss function independently; the total loss function of the multi-task learning model based on the convolutional neural network is the sum of the weighted loss functions of two inputs;
and 4, step 4: taking data in the side-scan sonar image data set and the optical image auxiliary data set as input of a multi-task learning model based on a convolutional neural network, setting network parameters and training the multi-task learning model based on the convolutional neural network to obtain an automatic classification network of the side-scan sonar image target;
and 5: verifying the automatic classification network performance of the side-scan sonar image target by using a verification set, and recording the highest accuracy; if the highest accuracy rate does not meet the requirement, adjusting the weight ratio of the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network, and returning to the step 4 for retraining;
step 6: and inputting the image detected by the side-scan sonar in real time into an automatic classification network of the side-scan sonar image target to obtain a classification result.
2. The side-scan sonar image classification method based on multitask learning according to claim 1, characterized in that:
in the step 3, a multitask learning model based on a convolutional neural network is constructed, the convolutional neural network is used for feature extraction, the convolutional neural network is constructed by imitating a biological visual mechanism, and the convolutional kernel sharing of the middle layer and the sparsity of interlayer connection enable the convolutional neural network to automatically and stably learn the image features with smaller calculated amount and have no additional feature engineering;
the loss functions of the two inputs of the multi-task learning model based on the convolutional neural network in the step 3 are specifically as follows:
the penalty function for the optical image auxiliary dataset classification task is:
wherein M is the number of auxiliary task samples in the optical image auxiliary data set participating in training; (x) is the fitting function of the convolved, pooled layers; g1(x) Fitting function of full connection layer for auxiliary task; x is the number ofs (i)The ith sample participating in training in the optical image auxiliary data set; y iss (i)A label corresponding to the ith sample in the optical image auxiliary data set;
the loss function for the side scan sonar image dataset classification task is:
wherein N is the number of target task samples in the side scan sonar image data set participating in training; (x) is the fitting function of the convolved, pooled layers; g2(x) A fitting function of a fully connected layer for the target task; x is the number oft (i)The ith sample participating in training is collected for the side scan sonar image data; y ist (i)A label corresponding to the ith sample in the side scan sonar image data set;
the final set total loss function of the convolutional neural network-based multitask learning model is:
J＝α*Js+β*Jt
α represents the weight of the auxiliary task and the target task, and is initialized to 1: 1.
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