CN113762349B - Marine organism-oriented lightweight aliasing dense network classification method and system - Google Patents
Marine organism-oriented lightweight aliasing dense network classification method and system Download PDFInfo
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Abstract
The invention relates to a marine organism-oriented lightweight aliasing dense network classification method and a marine organism-oriented lightweight aliasing dense network classification system, which comprise the steps of obtaining marine organism images, loading the marine organism images into an aliasing dense network model which is built and trained in advance, and obtaining classification results; the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, the aliasing network units comprise a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network units and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function. Compared with the prior art, the method has the advantages of acquiring more useful information, reducing parameters of the model, realizing information fusion, improving network classification accuracy, accelerating network training speed and the like.
Description
Technical Field
The invention relates to the field of image classification, in particular to a lightweight aliasing dense network classification method and system for marine organisms.
Background
Identification of marine fish species, habitat distribution, spawning area distribution, etc. are important research contents of marine biological science and marine environmental science. Information science and technology has become an important means for marine biologists and environmental scholars to conduct the above-described research. The core problem here is how to accurately classify marine fish targets based on the visual data obtained.
There are many fish species in the ocean that are highly visually alike and are difficult to distinguish accurately without specialized marine biological knowledge.
Most of the existing methods for classifying marine organisms through neural networks have the defects of large calculated amount, high energy consumption, poor classification accuracy and the like.
Disclosure of Invention
The invention aims to overcome the defects of large calculation amount, high energy consumption, poor classification accuracy and the like in the prior art and provides a lightweight aliasing dense network classification method and system for marine organisms.
The aim of the invention can be achieved by the following technical scheme:
a lightweight aliasing dense network classification method for marine organisms comprises the steps of obtaining marine organism images to be classified, loading the marine organism images into an aliasing dense network model which is built and trained in advance, and obtaining classification results;
the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, each aliasing network unit comprises a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network unit and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function.
Further, the convolution kernels of the first packet convolution layer and the second packet convolution layer are each 1×1 in size.
Further, the convolution kernel of the depth separable convolution layer is 3×3, the step length is 2, an average pooling layer is further arranged in a connection line between the serial connection layer and the input of the aliasing network unit, and the average pooling layer is 3×3, and the step length is 2.
Further, the convolution kernel of the depth separable convolution layer has a size of 3×3 and a step size of 1, and the concatenation layer is connected to the input of the aliasing network unit.
Further, the aliasing dense network model is provided with at least two dense blocks which are symmetrically distributed.
The invention also provides a lightweight aliasing dense network classification system for marine organisms, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the following steps: the method comprises the steps of obtaining marine organism images to be classified, loading the marine organism images into a pre-established and trained aliasing dense network model, and obtaining classification results;
the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, each aliasing network unit comprises a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network unit and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function.
Further, the convolution kernels of the first packet convolution layer and the second packet convolution layer are each 1×1 in size.
Further, the convolution kernel of the depth separable convolution layer is 3×3, the step length is 2, an average pooling layer is further arranged in a connection line between the serial connection layer and the input of the aliasing network unit, and the average pooling layer is 3×3, and the step length is 2.
Further, the convolution kernel of the depth separable convolution layer has a size of 3×3 and a step size of 1, and the concatenation layer is connected to the input of the aliasing network unit.
Further, the aliasing dense network model is provided with at least two dense blocks which are symmetrically distributed.
Compared with the prior art, the invention has the following advantages:
(1) In order to acquire more useful information and reduce parameters of a model as much as possible, the invention introduces separable convolution in a neural network for marine organism classification to realize the increase of a characteristic diagram and reduce the parameters of the model; to overcome the defect that the performance of the network model is seriously affected by the fact that in the deep convolution, the input channels of each group come from only a part of channels of input information, a channel aliasing structure is introduced. After the depth separation convolution is obtained, the structure divides each group into smaller subgroups, and then sends the subgroup of each group to each group of the next depth convolution; in this way, the effective fusion of the multi-channel information is improved while the amount of parameters of the network is reduced.
(2) According to the invention, 1 multiplied by 1 grouping convolution is respectively added at the head end and the tail end of the separable convolution and channel aliasing structure, and dimension reduction and recovery operations are carried out, so that an aliasing network unit is formed, a plurality of aliasing network units form a dense block in a dense connection mode, an aliasing dense network model is constructed through the dense block, and the classification of marine organisms by adopting the model has the advantages of small calculated amount, high classification accuracy and the like, and the classification performance is effectively improved under the condition of limited resources.
Drawings
Fig. 1 is a schematic structural diagram of two kinds of aliasing network units according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an aliased dense network model according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
In this embodiment, the classification method of marine fish is to be explored from the coarse granularity and the fine granularity, the classification technology is used to determine the classification of marine fish, and the classification technology is used to determine the classification of marine fish, namely the classification of the coarse granularity classification and the classification of the fine granularity classification. In coarse granularity family classification, a novel convolutional neural network model suitable for coarse classification is researched by introducing the ideas of channel aliasing and dense connection, and network training speed is accelerated while network classification precision is improved.
In order to accelerate the training of a network, a network model can obtain a better prediction result under the conditions of poor computing capability and low energy consumption, and the embodiment provides a lightweight aliasing dense network classification method for marine organisms, which comprises the steps of acquiring marine organism images to be classified, loading the marine organism images into a pre-established and trained aliasing dense network model, and acquiring a classification result;
the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, the aliasing network units comprise a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network units and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function.
The convolution kernels of the first packet convolution layer and the second packet convolution layer are 1×1 in size and are used for performing dimension reduction and recovery operations respectively.
The size of the convolution kernel of the depth separable convolution layer is 3×3; when the step length of the depth separable convolution layer is 1, the concatenation layer is directly connected with the input of the aliasing network unit; when the step length of the depth separable convolution layer is 2, an average pooling layer is further arranged in a connecting line of the input of the cascade layer and the aliasing network unit, and the average pooling layer is 3×3 in size and has the step length of 2.
The above-mentioned dense connection mode is specifically that the outputs of the preceding aliased network elements are all used as inputs of the following aliased network elements.
Detailed description of the conception:
in this embodiment, separable convolutions are introduced in order to obtain more useful information and minimize the parameters of the model. The separable convolution is to add a 1 x 1 point-by-point convolution after the above-described depth convolution, and by this layer an increase in the feature map is achieved and the parameters of the model are reduced. However, there is a serious problem in the deep convolution: the input channels of each group come from only a portion of the channels of the input information, which would severely impact the performance of the network model. To solve this problem, a channel-aliased structure is introduced. After the depth separation convolution is obtained, the structure divides each group into smaller subgroups and then sends the subgroups of each group to each group of the next depth convolution. In this way, different sets of information fusion can be achieved.
Finally, this embodiment combines the ideas of two structures of depth separable convolution and channel aliasing, and proposes an aliasing network element, as shown in fig. 1. Wherein, the left and right units respectively represent that different step sizes are adopted in the convolution process. Each cell starts and ends with a 1 x 1 block convolution (Group Convolution, gconv) and a dimension recovery operation, respectively, passing through BN (Batch Normalization, batch regularization) layer, channel aliasing (Channel Shuffle), depth separable convolution (Depth-Wise Separable Convolution, DWConv), etc., respectively, and finally concatenating with the input of each cell through a concatenation operation (Concat). A BN (Batch Normalization, batch regularization) layer is arranged after the 1×1 group convolution at the beginning of each unit, the depth separable convolution layer and the 1×1 group convolution at the end of each unit; the 1 x 1 packet convolution at the beginning of each cell and the output after the concatenation operation are provided with a Relu activation function. The plurality of aliasing network units form a Dense Block (Dense Block) by means of Dense connection. Finally, by the dense blocks, an aliased dense network model is constructed, as shown in fig. 2.
The aliasing dense network model is provided with at least two dense blocks which are symmetrically distributed.
In this embodiment, the aliasing dense network model is sequentially provided with a convolution layer, a dense block, a convolution layer, a max-pooling layer … (a combination of the convolution layer and the max-pooling layer), a dense block, a max-pooling layer, and a full-connection layer, and finally outputs a final prediction result.
The embodiment also provides a lightweight aliased dense network classification system facing marine organisms, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the lightweight aliased dense network classification method facing marine organisms.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (8)
1. A marine organism-oriented lightweight aliasing dense network classification method is characterized by comprising the steps of obtaining marine organism images to be classified, loading the marine organism images into a pre-established and trained aliasing dense network model, and obtaining classification results;
the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, the aliasing network units comprise a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network units and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function;
the size of the convolution kernel of the depth separable convolution layer is 3 multiplied by 3, the step length is 2, an average pooling layer is further arranged in a connecting line of the serial connection layer and the input of the aliasing network unit, and the average pooling layer is 3 multiplied by 3, and the step length is 2.
2. The marine organism-oriented lightweight aliased dense network classification method of claim 1, wherein the convolution kernels of the first and second packet convolution layers are each 1 x 1 in size.
3. A lightweight aliased dense network classification method for marine organisms according to claim 1, wherein the convolution kernel of the depth separable convolution layer is 3 x 3 in size and 1 in step size, and the concatenation layer connects the inputs of the aliased network elements.
4. The marine organism-oriented lightweight aliased dense network classification method of claim 1, wherein the aliased dense network model is provided with at least two symmetrically distributed dense blocks.
5. A marine organism-oriented lightweight aliased dense network classification system, comprising a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of: the method comprises the steps of obtaining marine organism images to be classified, loading the marine organism images into a pre-established and trained aliasing dense network model, and obtaining classification results;
the aliasing dense network model is a convolutional neural network, a dense block is arranged in the convolutional neural network, the dense block comprises a plurality of aliasing network units, the aliasing network units are connected in a dense connection mode, the aliasing network units comprise a first grouping convolutional layer, a batch regularization layer, a channel mixing layer, a depth separable convolutional layer, a second grouping convolutional layer and a serial connection layer which are sequentially connected, the serial connection layer is respectively connected with the input of the aliasing network units and the output of the second grouping convolutional layer, and the output of the serial connection layer is connected with a linear rectification function;
the size of the convolution kernel of the depth separable convolution layer is 3 multiplied by 3, the step length is 2, an average pooling layer is further arranged in a connecting line of the serial connection layer and the input of the aliasing network unit, and the average pooling layer is 3 multiplied by 3, and the step length is 2.
6. The marine organism-oriented lightweight aliased dense network classification system of claim 5, wherein the convolution kernels of the first and second packet convolution layers are each 1 x 1 in size.
7. A marine organism oriented lightweight aliased dense network classification system of claim 5, wherein the convolution kernel of the depth separable convolution layer is 3 x 3 in steps of 1, the concatenation layer connecting the inputs of the aliased network elements.
8. The marine organism-oriented lightweight aliased dense network classification system of claim 5, wherein the aliased dense network model is provided with at least two symmetrically distributed dense blocks.
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