CN111144318A - Point cloud data noise reduction method for underwater sonar system - Google Patents

Point cloud data noise reduction method for underwater sonar system Download PDF

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CN111144318A
CN111144318A CN201911380561.8A CN201911380561A CN111144318A CN 111144318 A CN111144318 A CN 111144318A CN 201911380561 A CN201911380561 A CN 201911380561A CN 111144318 A CN111144318 A CN 111144318A
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point cloud
cloud data
data
noise
noise reduction
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CN111144318B (en
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吕杰
黄凯钢
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Suzhou Lianshitai Electronic Information Technology Co ltd
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
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    • G06F2218/02Preprocessing
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses a point cloud data noise reduction method for an underwater sonar system, which comprises the following steps: (1) based on the underwater acoustic signal reflection principle, removing discrete data points without connected areas in the point cloud data; (2) performing side lobe effect suppression on the point cloud data; (3) removing noise based on the correlation of continuous multi-frame point cloud data; (4) dynamically normalizing the point cloud data to process and remove noise data and background data; (5) and removing point cloud data which are not meaningful physically according to the actual physical meaning of the three-dimensional point cloud data aiming at the point cloud data of the three-dimensional data structure. Different noise reduction strategies are adopted for different noises respectively so as to realize comprehensive noise reduction of the point number data and improve the noise reduction effect.

Description

Point cloud data noise reduction method for underwater sonar system
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a point cloud data noise reduction method for an underwater sonar system.
Background
The underwater sonar imaging is based on sonar point cloud data, so the processing quality of the point cloud data directly determines underwater sonar imaging and a series of subsequent sonar data processing processes, including extracting useful target information from the point cloud data, detecting objects in water and the like. The underwater ultrasonic signals can generate specific underwater acoustic effects including side lobe effects, reverberation effects and the like in principle, and certain noise signals can be generated in the signal acquisition and signal transmission processes, so that the problem of data noise cannot be thoroughly solved fundamentally. The interference of noise brings certain difficulty to algorithms such as target detection, identification, tracking and the like, and the point cloud data needs to be subjected to certain noise reduction processing in a data preprocessing module, so that the subsequent process can be greatly facilitated.
The patent application with the application publication number of CN105785349A discloses a noise removal method for a phased array three-dimensional acoustic camera sonar, which is used for uniformly removing noise without classification and denoising, and can cause poor denoising effect.
The patent application with the application publication number of CN109035224A discloses a submarine pipeline detection and three-dimensional reconstruction method based on multi-beam point cloud, in the submarine pipeline detection and three-dimensional reconstruction method, a point cloud denoising and filtering method based on density analysis is adopted to denoise point cloud data of a pipeline, and the denoising effect of the single denoising method is not good enough.
Disclosure of Invention
The invention aims to provide a point cloud data noise reduction method for an underwater sonar system, which adopts different noise reduction strategies for different noises to realize comprehensive noise reduction of point data and improve the noise reduction effect.
In order to realize the aim of the invention, the invention provides the following technical scheme:
a method for reducing noise of point cloud data of an underwater sonar system comprises the following steps:
(1) based on the underwater acoustic signal reflection principle, removing discrete data points without connected areas in the point cloud data;
(2) performing side lobe effect suppression on the point cloud data;
(3) removing noise based on the correlation of continuous multi-frame point cloud data;
(4) dynamically normalizing the point cloud data to process and remove noise data and background data;
(5) and removing point cloud data which are not meaningful physically according to the actual physical meaning of the three-dimensional point cloud data aiming at the point cloud data of the three-dimensional data structure.
Compared with the prior art, the invention has the following beneficial technical effects:
(1) the point cloud data noise reduction method for the underwater sonar system can obviously improve the signal effectiveness and can effectively inhibit noise signals in data, so that the noise reduction method can provide effective data support for a subsequent data processing flow and has important significance for the underwater sonar system.
(2) The method for reducing the noise of the point cloud data of the underwater sonar system is simple to implement, carries out noise reduction processing on specific scenes aiming at the noise characteristics in the point cloud data of the underwater sonar system, the flow of the underwater sonar system and the like by using the conventional simple filtering technology, and has strong pertinence and good practical effect.
(3) The method for reducing the noise of the point cloud data of the underwater sonar system can also carry out fine adjustment according to the actual application scene so as to improve the noise removing effect of the point cloud data in the actual application scene.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of data processing steps of an underwater sonar system provided by an embodiment;
FIG. 2 is a schematic diagram of an embodiment of noise classification of point cloud data of an underwater sonar system;
FIG. 3 is a flow chart of a method for denoising point cloud data of an underwater sonar system according to an embodiment;
FIG. 4 is a flowchart of a side lobe effect suppression step in the method for denoising point cloud data of an underwater sonar system according to an embodiment;
FIG. 5 is a flowchart of dynamic normalization steps in the method for denoising cloud data of an underwater sonar system according to the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
FIG. 1 is a flow chart of data processing steps of an underwater sonar system provided by an embodiment. As shown in fig. 1, in a main processing flow of the underwater sonar system, an a/D chip acquires original signals, then beam forming processing is performed to change the original signals into a point cloud data type to be processed by the noise reduction method, then the noise reduction method is used to perform noise reduction processing on the point cloud data, and the processed data is subjected to obstacle detection, target identification, real-time image display and the like according to an actual application scene.
FIG. 2 is a schematic diagram of cloud data noise classification of an underwater sonar system according to an embodiment. As shown in fig. 2, the main noise in the point cloud data of the underwater sonar system is: side lobe effect caused by acoustic principle, Gaussian noise caused by line transmission, signal sampling and the like, signal reverberation caused by acoustic reflection diffraction and the like. Some of these signals are regularly noisy, such as side lobe effects. Some are random noise such as signal reflection, diffraction noise, transmission noise, which are randomly distributed.
FIG. 3 is a flowchart of a method for denoising cloud data of an underwater sonar system according to an embodiment. As shown in fig. 3, the method for reducing noise of point cloud data of an underwater sonar system comprises the following steps:
s101, based on the underwater acoustic signal reflection principle, removing discrete data points without connected areas in the point cloud data.
Based on the principle of underwater acoustic signal reflection, after the underwater acoustic signal irradiates a target object, a certain diffraction phenomenon is generated around the object, and based on the characteristic, a point which has higher single-point signal intensity but is not related to a surrounding array element signal is considered as a noise point and is removed. Through getting rid of the discrete point that does not have the connected region, can get rid of most discrete noise signal in the point cloud data, to sonar point cloud signal under water, can reach comparatively ideal preliminary denoising effect through preliminary treatment.
Noise of point cloud data of the sonar system is mainly some Gaussian noise, and on the basis of S101, noise reduction processing is performed on the point cloud data by adopting a mean filtering method so as to reduce random noise points in the point cloud data. The method can perform filtering operation on a small part of the noise still existing in S101.
S102, performing side lobe effect suppression on the point cloud data.
For the side lobe effect in the acoustic effect, according to the side lobe characteristic, the first side lobe value is far smaller than the main lobe value, the side lobe value is removed or reduced to remove noise pollution caused by the side lobe effect, almost all strong target data points have the side lobe effect, and therefore the step is necessary and relatively effective.
For target data, the data relative magnitude of the target main lobe is consistent, and for background non-target data, the relative magnitude is lower. The sidelobe suppression can be effectively realized by removing data points smaller than a certain threshold value of the main lobe signal in the same layer level. The side lobe is suppressed, and other useful data cannot be suppressed, so that the method has better practical operability.
Fig. 4 is a flowchart of a side lobe effect suppression step in the point cloud data noise reduction method of the underwater sonar system provided by the embodiment, and as shown in fig. 4, the side lobe effect suppression step includes:
aiming at the point cloud data collected in each layer, point cloud data with the same physical distance are taken out, the point cloud data with the maximum value is found out, the preset percentage of the maximum value is used as a screening threshold, all the point cloud data in each layer are traversed, and the point cloud data exceeding the screening threshold corresponding to each layer are removed, so that noise points caused by side lobe effects are removed;
besides removing noise points caused by side effects, the side lobe effect suppression step further comprises the following steps: and multiplying the point cloud data which are smaller than the maximum preset percentage by a certain coefficient to reduce noise caused by the side lobe effect.
And S103, removing noise based on the correlation of the continuous multi-frame point cloud data.
Because point cloud data with a target exists in continuous multiframes, and for noise, the data has relative randomness in the continuous multiframes, a multiframe filtering method can be adopted to remove random noise without correlation, and the specific process is as follows:
directly removing point cloud data with low correlation in continuous multi-frame data;
and removing noise in the image by a median filtering method at the same position of the continuous multiframe data aiming at the point cloud data with higher correlation in the continuous multiframe data, namely replacing the data value of each frame at the position by using the data mean value at the same position in the continuous multiframe data.
In the embodiment, the point cloud data with the occurrence frequency less than the preset frequency in each group of continuous n frame data is regarded as the point cloud data with low correlation, the remaining point cloud data is the data with high correlation, and the preset frequency is generally less than 1/2 n.
When the data source is dynamic, that is to say when gathering the sonar and being the motion type, when detecting the sensor and being relative motion for the target waters promptly, among the continuous multiframe data, probably because the relative motion of detecting the sensor can cause the sonar data that same target corresponds to appear in different positions, can cause misjudgement to target and noise like this, consequently to the dynamic point cloud data of gathering, before carrying out the relevance based on continuous point cloud multiframe data and getting rid of the noise, correct the information to dynamic point cloud data according to the gesture of detecting the sensor. Add sonar offset into sonar point cloud data through attitude sensor promptly to realize the correction to dynamic point cloud data, and then guarantee that the data between the multiframe data have relevant comparability.
When the continuous multi-frame correlation processing is carried out, for each group of continuous multi-frames, the data which is not overlapped with the previous frame after the offset value is added into the point cloud data still retains the original data, because the data may be used in the correlation processing process of each group of continuous multi-frames.
Based on the sonar detection principle, the detection range can be as far as hundreds of meters, and the offset between continuous multiframe is often only a few meters, so the overlap ratio of point cloud data between continuous multiframe is relatively high, and therefore, the usability of removing noise based on the correlation of continuous multiframe point cloud data is very strong.
And S104, dynamically normalizing the point cloud data to remove noise data and background data.
Specifically, as shown in fig. 5, the point cloud data processed in S101 to S103 is subjected to data intensity value statistics, and the average value of the statistical data intensity values is multiplied by a preset percentage to serve as a normalized screening threshold, so that all the point cloud data are facilitated, and the point cloud data exceeding the normalized screening threshold are removed, so that the point cloud data are subjected to dynamic normalization operation to remove noise data and background data.
In the embodiment, through the dynamic normalization operation on the point cloud data, noise data with low relative intensity value and background data without targets are further filtered, and target data in the point cloud data can be better highlighted.
And S105, removing point cloud data which are not meaningful physically according to the actual physical meaning of the three-dimensional point cloud data aiming at the point cloud data of the three-dimensional data structure.
For point cloud data with a three-dimensional data structure, further processing can be carried out according to its physical meaning. Compared with two-dimensional point cloud data, the three-dimensional point cloud data increases actual physical depth information, and points which are not meaningful physically can be removed based on the characteristics. Specifically, point cloud data that is beyond the water surface and below the water bottom surface in the depth direction may be removed.
The method for denoising the point cloud data of the underwater sonar system can obviously improve the signal effectiveness and can effectively suppress noise signals in the data, so that the denoising method can provide effective data support for the subsequent data processing flow and has important significance for the underwater sonar system.
The method for reducing the noise of the point cloud data of the underwater sonar system is simple to implement, carries out noise reduction processing on specific scenes aiming at the noise characteristics in the point cloud data of the underwater sonar system, the flow of the underwater sonar system and the like by using the conventional simple filtering technology, and is strong in pertinence and good in practical effect.
The method for denoising the point cloud data of the underwater sonar system can also finely adjust the screening threshold according to the actual application scene so as to improve the denoising effect of the point cloud data in the actual application scene.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.

Claims (8)

1. A point cloud data noise reduction method of an underwater sonar system is characterized by comprising the following steps:
(1) based on the underwater acoustic signal reflection principle, removing discrete data points without connected areas in the point cloud data;
(2) performing side lobe effect suppression on the point cloud data;
(3) removing noise based on the correlation of continuous multi-frame point cloud data;
(4) dynamically normalizing the point cloud data to process and remove noise data and background data;
(5) and removing point cloud data which are not meaningful physically according to the actual physical meaning of the three-dimensional point cloud data aiming at the point cloud data of the three-dimensional data structure.
2. The underwater sonar system point cloud data noise reduction method according to claim 1, wherein on the basis of the step (1), a mean filtering method is adopted to perform noise reduction processing on the point cloud data so as to reduce random noise points in the point cloud data.
3. The underwater sonar system point cloud data noise reduction method according to claim 1, wherein the side lobe effect suppressing step includes:
and aiming at the point cloud data collected in each layer, point cloud data with the same physical distance is taken out, the point cloud data with the maximum value is found out, the preset percentage of the maximum value is used as a screening threshold, all the point cloud data in each layer are traversed, and the point cloud data exceeding the screening threshold corresponding to each layer are removed, so that noise caused by side lobe effect is removed.
4. The underwater sonar system point cloud data noise reduction method according to claim 1, wherein the side lobe effect suppressing step further includes: and multiplying the point cloud data which are smaller than the maximum preset percentage by a certain coefficient to reduce noise caused by the side lobe effect.
5. The underwater sonar system point cloud data noise reduction method according to claim 1, wherein the specific process of step (3) is as follows:
and directly removing point cloud data with low correlation in continuous multi-frame data.
And removing noise in the image by a median filtering method at the same position of the continuous multiframe data aiming at the point cloud data with higher correlation in the continuous multiframe data, namely replacing the data value of each frame at the position by using the data mean value at the same position in the continuous multiframe data.
6. The method for reducing the noise of the point cloud data of the underwater sonar system according to claim 1 or 5, wherein the collected dynamic point cloud data is corrected according to posture correction information of a detection sensor before noise is removed based on correlation of continuous multi-frame point cloud data.
7. The method for reducing noise of point cloud data of an underwater sonar system according to claim 1, wherein when continuous multi-frame correlation processing is performed, for each group of continuous multi-frames, original data are still retained after an offset value is added to the point cloud data and the data are not overlapped with previous frames.
8. The underwater sonar system point cloud data noise reduction method according to claim 1, wherein in the step (4), data intensity values of the point cloud data after the point cloud data are processed in the steps (1) to (3) are counted, and the average value of the data intensity values is multiplied by a preset percentage to serve as a normalized screening threshold value, so that all the point cloud data are facilitated, and the point cloud data exceeding the normalized screening threshold value are removed, so that dynamic normalization operation is performed on the point cloud data to remove noise data and background data.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111681667A (en) * 2020-06-23 2020-09-18 青岛科技大学 Underwater sound signal denoising method based on adaptive window filtering and wavelet threshold optimization
WO2022186103A1 (en) * 2021-03-02 2022-09-09 パイオニア株式会社 Information processing device, information processing method, program, and storage medium
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

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106643671A (en) * 2016-12-01 2017-05-10 江苏省测绘工程院 Underwater point cloud denoising method based on airborne LiDAR depth sounding system
CN109035224A (en) * 2018-07-11 2018-12-18 哈尔滨工程大学 A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106643671A (en) * 2016-12-01 2017-05-10 江苏省测绘工程院 Underwater point cloud denoising method based on airborne LiDAR depth sounding system
CN109035224A (en) * 2018-07-11 2018-12-18 哈尔滨工程大学 A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
牛蕾: "SAR图像舰船目标去旁瓣处理" *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111681667A (en) * 2020-06-23 2020-09-18 青岛科技大学 Underwater sound signal denoising method based on adaptive window filtering and wavelet threshold optimization
CN111681667B (en) * 2020-06-23 2021-05-04 青岛科技大学 Underwater sound signal denoising method based on adaptive window filtering and wavelet threshold optimization
WO2022186103A1 (en) * 2021-03-02 2022-09-09 パイオニア株式会社 Information processing device, information processing method, program, and storage medium
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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