CN117075092A - Underwater sonar side-scan image small target detection method based on forest algorithm - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
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- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
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- G01S15/8902—Side-looking sonar
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Abstract
The invention relates to the technical field of underwater multi-beam sonar, and discloses an underwater sonar side-scan image small target detection method based on a forest algorithm, which is characterized by comprising the following steps of: step one, using information with a sonar graphic target to establish a sonar side-scan graphic set with target information; and secondly, randomly selecting N samples from the sonar side-scan pattern set to form a new target detection data subset. The technical scheme of the invention adopts a forest algorithm, and the forest algorithm has the following advantages: (1) The growth of the tree in the random forest algorithm gives additional randomness to the model, creating diversity, each node is segmented into optimal features that minimize errors, the random forest selects random features to construct the optimal segmentation, only a random subset of the nodes is considered for segmentation, and the tree can even be made more random by using random thresholds on each feature, rather than searching for optimal thresholds as in a normal decision tree.
Description
Technical Field
The invention relates to the technical field of underwater multi-beam sonar, in particular to an underwater sonar side-scan image small target detection method based on a forest algorithm.
Background
The multi-beam sonar technology is an advanced sonar technology, and can simultaneously transmit a plurality of acoustic beams, so that the detection efficiency and the detection precision of sonar are improved. The technology is widely applied to the fields of sea exploration, submarine topography mapping, underwater target detection and the like.
The use of multi-beam sonar technology is very widespread, particularly in marine exploration and sub-sea topographic mapping. In marine exploration, multi-beam sonar technology can help scientists to more accurately detect seafloor topography and marine life. In terms of submarine topography mapping, multi-beam sonar technology can help scientists to map submarine topography more accurately.
However, manually analyzing the massive amount of underwater sonar image data generated every day is a tedious and time-consuming task, and the large number of small targets greatly increases the workload. Therefore, a small target detection system for a classified and identified sonar side-scan image has important practical value for reducing time-consuming and expensive manual input.
Disclosure of Invention
The invention mainly aims to provide a method for detecting a small target of an underwater sonar side-scan image based on a forest algorithm, which aims to detect the small target in a mode of combining target identical target information and filtering target information which does not belong to the return characteristic, effectively improves the small target detection performance of the underwater sonar side-scan image, and has practicability and reliability.
In order to achieve the purpose, the underwater sonar side-scan image small target detection method based on the forest algorithm is characterized by comprising the following steps of:
step one, using information with a sonar graphic target to establish a sonar side-scan graphic set with target information;
randomly selecting N samples from the sonar side-scan pattern set to form a new target detection data subset;
randomly selecting M features from all features to be selected of the sonar graph by each data subset, and selecting features conforming to an input image from the M features as input features of a decision tree;
splitting according to the selected characteristics to obtain a child node;
step five, repeating the step 2-3, respectively obtaining corresponding decision trees through each data subset, and forming a random forest by a plurality of decision trees together;
step six, counting the number of each characteristic by an algorithm, and selecting the class with the largest number as a result to return;
and step seven, combining the classification results, and detecting the small target of the underwater sonar side-scan image by combining the same target information of the targets and filtering the target information which does not belong to the return characteristic.
In an embodiment, in the step one, fuzzy standards are performed on all targets in the collected sonar graphic information by adopting rectangular frames, large targets are marked one by one through the rectangular frames, and small targets are marked in a region through the rectangular frames.
In one embodiment, each internal node in the decision tree represents a feature, each branch represents a value for this effect, and each leaf node represents a classification.
In one embodiment, each subset in the second step contains data with the same characteristics, and a classification model based on random forests is implemented by using scikit-learn library of Python.
In one embodiment, the sonar side-scan pattern set in the step one uses an Iris dataset model, and the three categories in the dataset are Iris Setosa, iris Versicolour, and Iris virginiana respectively, and the dataset has 150 samples, wherein each category has 50 samples.
In one embodiment, the second step also requires converting the dataset into a DataFrame format using the Pandas library, and setting the number of trees and the maximum depth of the trees as default parameters.
In one embodiment, the sonar graphic features are texture, shape, and edges.
In an embodiment, in the fifth step, the data set is divided into a training set and a testing set, the training set is used for training the model, and the testing set is used for testing the prediction accuracy of the model.
In an embodiment, the training set trains N weak learners independently, and the final strong learners are obtained for the N weak learners by a set strategy, the prediction classes are { c1, c2, … … ck }, and the prediction results of the N weak learners are (h) for any one prediction sample x 1 （x），h 2 （x），……h T （x））。
In one embodiment, each individual learner has a weight w, then the final prediction is:
wherein,H(x)represents the final prediction result corresponding to the prediction sample x, w i Is individual studyDevice h i Is usually:
；
wherein,Trepresenting the number of samples.
The technical scheme of the invention has the following advantages by adopting a forest algorithm:
(1) The growth of trees in random forest algorithms can bring additional randomness to the model, creating diversity. Each node is segmented into optimal features that minimize errors, and a random forest selects random features to construct the optimal segmentation. Therefore, the detection method only considers the random subset used for dividing the node, the tree can be made more random by using a random threshold value on each feature, the optimal threshold value is not searched as a normal decision tree, and features can be better refined when the underwater small target side-scanning image is processed, so that the discrimination is improved.
(2) The relative importance of each feature to the prediction is easily measured. It measures the importance of a feature by looking at how much of the non-purity of all trees in a forest is reduced by using the feature. The method automatically calculates the score of each feature after training, and normalizes the results so that the sum of the importance of all the features is equal to 1, so that the weight of each feature can be considered when small target identification is carried out, and the method achieves fine processing and is more practical for small target image processing.
(3) The processing capability of the high-dimensional data set is good, thousands of input variables can be processed, the most important variables are determined, the multi-feature characteristics of the small target image are further processed, and the recognition degree is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an algorithm flow chart of the underwater sonar side-scan image small target detection method based on the forest algorithm.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all 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.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present invention, the directional indications are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and/or" as it appears throughout includes three parallel schemes, for example "A and/or B", including the A scheme, or the B scheme, or the scheme where A and B are satisfied simultaneously. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1, in an embodiment of the invention, a method for detecting a small target of an underwater sonar side-scan image based on a forest algorithm is characterized by comprising the following steps:
step one, using information with a sonar graphic target to establish a sonar side-scan graphic set with target information;
randomly selecting N samples from the sonar side-scan pattern set to form a new target detection data subset;
randomly selecting M features from all features to be selected of the sonar graph by each data subset, and selecting features conforming to an input image from the M features as input features of a decision tree;
splitting according to the selected characteristics to obtain a child node;
step five, repeating the step 2-3, respectively obtaining corresponding decision trees through each data subset, and forming a random forest by a plurality of decision trees together;
step six, counting the number of each characteristic by an algorithm, and selecting the class with the largest number as a result to return;
and step seven, combining the classification results, and detecting the small target of the underwater sonar side-scan image by combining the same target information of the targets and filtering the target information which does not belong to the return characteristic.
The technical scheme of the invention has high robustness to noise, abnormal values and other adverse factors through the random forest algorithm, the random forest algorithm trains the data by using a plurality of decision trees at the same time, and a more stable and reliable prediction result can be obtained through an averaging or voting mechanism. In addition, the random forest algorithm can automatically process missing values in the data set, and robustness of the algorithm is further enhanced. The random forest algorithm can effectively avoid the problem of overfitting by using a random subset and random features, each decision tree is trained on different random subsets, so that the difference between each decision tree is larger, the variance of a model is reduced, the random forest algorithm can process high-dimensional data, only a part of random features are selected for training, the random forest algorithm does not need to calculate all the features, the algorithm efficiency can be improved, and the random forest algorithm can evaluate the importance of the features by calculating the importance of each feature in all the decision trees. This importance index may help us select the most relevant features, thereby improving the efficiency and accuracy of the algorithm.
In general, each target in the collected sonar graphic information is subjected to fuzzy standard by adopting a rectangular frame, large targets are marked one by one through the rectangular frame, and small targets are marked in a region through the rectangular frame.
Accordingly, each internal node in the decision tree represents a feature, each branch represents a value for this effect, and each leaf node represents a classification.
In this embodiment, each subset contains data with the same characteristics, and a classification model based on random forests is implemented by using the scikit-learn library of Python.
Of course, the sonar side-scan pattern set adopts an Iris data set model, three categories in the data set are Iris Setosa, iris Versicolour and Iris Virginiana respectively, the data set has 150 samples, wherein each category has 50 samples
In this embodiment, it is also necessary to use the Pandas library to convert the dataset into the DataFrame format, and set the number of trees and the maximum depth of the trees as default parameters
Further, sonar graphics features are texture, shape, and edges.
However, the design is not limited thereto, and in other embodiments, the training set is independently trained on the basis of the aboveN weak learners, and obtaining final strong learners for the N weak learners through an aggregation strategy, wherein the prediction categories are { c1, c2, … … ck }, and the prediction results of the N weak learners are (h) for any one prediction sample x 1 （x），h 2 （x），……h T （x））。
In this embodiment, each individual learner has a weight w, and the final prediction is:
wherein,H(x)represents the final prediction result corresponding to the prediction sample x, w i Is an individual learner h i Is usually:
；
wherein,Trepresenting the number of samples.
It should be noted that, the random forest is an integrated learning algorithm, and is widely used in fields such as data mining and machine learning due to its excellent performance. The random forest trains the data set by using a plurality of decision trees at the same time, and obtains a final prediction result by a voting mechanism or an averaging mode, wherein the random mainly refers to two aspects: first, randomly selecting samples, namely, sampling with replacement from an original data set to obtain sub data sets, wherein the sample size of the sub data sets is kept consistent with that of the original data set, elements among different sub data sets can be repeated, and elements among the same sub data set can also be repeated. Second, randomly selecting features, similar to the random sample selection process, the sub-dataset selects a certain number of feature subsets from all the original features to be selected, and then selects from the re-selected feature subsets. And forming a decision tree through the data subset and the feature subset selected each time, and finally obtaining a random forest algorithm.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (10)
1. The underwater sonar side-scan image small target detection method based on the forest algorithm is characterized by comprising the following steps of:
step one, using information with a sonar graphic target to establish a sonar side-scan graphic set with target information;
randomly selecting N samples from the sonar side-scan pattern set to form a new target detection data subset;
randomly selecting M features from all features to be selected of the sonar graph by each data subset, and selecting features conforming to an input image from the M features as input features of a decision tree;
splitting according to the selected characteristics to obtain a child node;
step five, repeating the step 2-3, respectively obtaining corresponding decision trees through each data subset, and forming a random forest by a plurality of decision trees together;
step six, counting the number of each characteristic by an algorithm, and selecting the class with the largest number as a result to return;
and step seven, combining the classification results, and detecting the small target of the underwater sonar side-scan image by combining the same target information of the targets and filtering the target information which does not belong to the return characteristic.
2. The method for detecting the small targets in the underwater sonar side-scan image based on the forest algorithm according to claim 1, wherein in the first step, fuzzy standards are carried out on all targets in the collected sonar graphic information by adopting rectangular frames, large targets are marked one by one through the rectangular frames, and small targets are marked in a region through the rectangular frames.
3. A method for detecting a small target in an underwater sonar sideways scanned image based on a forest algorithm as defined in claim 1, wherein each internal node in said decision tree represents a feature, each branch represents a value of the special effect, and each leaf node represents a classification.
4. The underwater sonar side-scan image small target detection method based on the forest algorithm as set forth in claim 1, wherein each subset in the second step contains data with the same characteristics, and a classification model based on random forests is realized by adopting a scikit-learn library of Python.
5. The method for detecting the small target of the underwater sonar side-scan image based on the forest algorithm as set forth in claim 1, wherein the sonar side-scan image set in the step one adopts an Iris data set model, and three categories in the data set are Iris Setosa, iris verisimolour and Iris virginiana respectively, and the data set has 150 samples, wherein each category has 50 samples.
6. The underwater sonar side-scan image small target detection method based on the forest algorithm as claimed in claim 1, wherein in the second step, the Pandas library is further required to be used for converting the data set into a DataFrame format, and the number of trees and the maximum depth of the trees are set as default parameters.
7. The underwater sonar side-scan image small target detection method based on the forest algorithm as defined in claim 1, wherein the sonar figure features are texture, shape and edge.
8. The underwater sonar side-scan image small target detection method based on the forest algorithm as claimed in claim 1, wherein in the fifth step, the data set is divided into a training set and a test set, the training set is used for training a model, and the test set is used for testing the prediction accuracy of the model.
9. The underwater sonar side-scan image small target detection method based on forest algorithm as claimed in claim 8, wherein said training set trains N weak learners independently, and the final strong learners are obtained for the N weak learners by means of a set strategy, the prediction class is { c1, c2, … … ck }, and the prediction results of the N weak learners for any one prediction sample x are (h 1 （x），h 2 （x），……h T （x））。
10. A method for detecting a small target in an underwater sonar side-scan image based on a forest algorithm as defined in claim 9, wherein each individual learner has a weight w, then the final prediction is:
wherein,H(x)represents the final prediction result corresponding to the prediction sample x, w i Is an individual learner h i Is usually:
；
wherein,Trepresenting the number of samples.
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| CN117538881A (en) * | 2024-01-10 | 2024-02-09 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
| CN117538881B (en) * | 2024-01-10 | 2024-05-07 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
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