Method and apparatus for processing scanning sonar data, and storage medium

Abstract

The present application provides a method and apparatus, and a storage medium for processing scanning sonar data. The method includes: acquiring a sound pulse detection data matrix of an underwater target collected in real time by a scanning sonar, arranging the sound pulse detection data matrix in a continuous angle order to form a grayscale matrix diagram; and performing processing based on the grayscale matrix diagram to extract the boundary of the underwater target. This method enables an underwater device to quickly extract boundary information of nearby obstacle boundaries in a complex and dynamic water area environment, for example, it can ensure the safe movement of the underwater device.

Description

Technical Field

[0001] Embodiments of the present application relate to underwater sonar technology, and more particularly, to a method and apparatus for processing scanned sonar data, and a storage medium. Background Art

[0002] The extraction of obstacle information is mostly used in path planning fields such as aircraft, automobiles, ships, etc. Common extraction devices include devices based on radar, infrared technology, and sensors such as cameras. These common devices are mainly used in the air and on land. However, the underwater environment is intricate, and the refractive index of the medium water is significantly different from that of air. Therefore, in the underwater environment, the process of extracting obstacle information (for example, the boundary of an obstacle) is more complex, and the devices used are different from those used in the air or on land. Commonly used underwater detection devices include mechanical scanning sonars. Different from underwater imaging, recognition, tracking and other technologies, mechanical scanning sonars obtain underwater environment information by emitting acoustic pulses underwater and then receiving the echoes reflected by each acoustic pulse from the objects in front of its propagation direction, so as to detect the underwater environment and the obstacles therein.

[0003] The boundary extraction of underwater obstacles mainly uses technologies such as image segmentation, deep learning, lidar, etc. However, the image segmentation processing speed is slow, the deep learning model is complex, and although lidar belongs to the same type of device as the mechanical scanning sonar, its working principle is different from that of the mechanical scanning sonar, and it is applicable to different working environments. Mechanical scanning sonars are mainly carried on moving devices to help avoid obstacles, and their requirements are different from those of side-scan sonars and imaging sonars (for example, used in scenarios such as terrain exploration and underwater imaging). Therefore, for specific underwater targets of mechanical scanning sonars, a faster and more efficient processing method is needed. It has certain prospects and values for applications in fields such as underwater exploration and marine scientific research. Summary of the Invention

[0004] At least one embodiment of the present application provides a method and apparatus for processing scanned sonar data, and a storage medium. The method enables an underwater device to quickly extract boundary information of nearby obstacle boundaries in a complex and dynamic water area environment, for example, to ensure the safe movement of the underwater device.

[0005] At least one embodiment of the present application provides a method for processing scanned sonar data, including: acquiring a matrix of acoustic pulse detection data of an underwater target collected in real time by a scanned sonar, arranging the matrix of acoustic pulse detection data in a continuous angular order to form a grayscale matrix diagram; and performing processing based on the grayscale matrix diagram to extract the boundary of the underwater target.

[0006] For example, in the method provided by at least one embodiment of the present application, forming a grayscale matrix diagram includes: selecting a target intensity threshold, screening the data in the acoustic pulse detection data matrix according to the target intensity threshold, and using the screened acoustic pulse detection data matrix to form a grayscale matrix diagram.

[0007] For example, in the method provided by at least one embodiment of the present application, screening the data in the acoustic pulse detection data matrix according to the target intensity threshold includes: setting the data in the acoustic pulse detection data matrix that is less than the target intensity threshold to a first predetermined value.

[0008] For example, in the method provided by at least one embodiment of the present application, the target intensity threshold is an adaptive intensity threshold.

[0009] For example, in the method provided by at least one embodiment of the present application, forming a grayscale matrix diagram further includes: removing the noise of the scanning sonar itself by setting the first n columns of the selected rows in the acoustic pulse detection data matrix to a second predetermined value, and wherein, n depends on the size of the scanning sonar.

[0010] For example, in the method provided by at least one embodiment of the present application, arranging the acoustic pulse detection data matrix in a continuous angle order includes: storing the continuous angles and the acoustic pulse detection data corresponding to the continuous angles in the form of key-value pairs into a dictionary.

[0011] For example, in the method provided by at least one embodiment of the present application, before processing based on the grayscale matrix diagram to extract the boundary of the underwater target, it further includes: performing enhancement processing on the formed grayscale matrix diagram to improve data fineness, and wherein, the enhanced grayscale matrix diagram is used to extract the boundary of the underwater target.

[0012] For example, in the method provided by at least one embodiment of the present application, performing enhancement processing on the formed grayscale matrix diagram includes: when the acoustic pulse detection data at the current acquisition time point is not obtained, using the acoustic pulse detection data collected at the previous acquisition time point to obtain the alternative acoustic pulse detection data at the current acquisition time point.

[0013] For example, in the method provided by at least one embodiment of the present application, performing enhancement processing on the formed grayscale matrix diagram further includes: performing interpolation processing on the formed grayscale matrix diagram.

[0014] For example, in the method provided by at least one embodiment of the present application, before processing based on the grayscale matrix diagram to extract the boundary of the underwater target, it further includes: performing filtering processing on the enhanced grayscale matrix diagram.

[0015] For example, in the method provided by at least one embodiment of the present application, before processing the grayscale matrix diagram to extract the boundary of the underwater target, it further includes: performing edge detection on the enhanced grayscale matrix diagram.

[0016] For example, in the method provided by at least one embodiment of the present application, the edge detection includes the canny detection method, and wherein, the upper and lower boundaries for edge detection are determined based on the determined intensity threshold.

[0017] For example, in the method provided by at least one embodiment of the present application, it further includes: according to the relationship between the continuous angle and the acoustic pulse detection data, mapping and calculating the grayscale matrix diagram into a scan diagram of the underwater target collected in real time by the scanning sonar.

[0018] At least one embodiment of the present application provides an apparatus for processing scanning sonar data, including: a scanning module configured to obtain a matrix of acoustic pulse detection data of an underwater target collected in real time by the scanning sonar, and arrange the matrix of acoustic pulse detection data in the order of continuous angles to form a grayscale matrix diagram; and a processing module configured to process the grayscale matrix diagram to extract the boundary of the underwater target.

[0019] At least one embodiment of the present application provides an apparatus for processing scanning sonar data, including: at least one processor; at least one memory having computer-executable instructions, wherein the computer-executable instructions, when executed by the at least one processor, execute the method described in any of the above embodiments.

[0020] At least one embodiment of the present application provides a storage having executable instructions, wherein the executable instructions, when executed by at least one processor, execute the method described in any of the above embodiments. Description of the Drawings

[0021] Through the following description of the embodiments in conjunction with the drawings, the aspects, features, and advantages of the present application will become clearer and easier to understand, wherein:

[0022] Figure 1 Shows a flowchart of a method for processing scanning sonar data according to at least one embodiment of the present application;

[0023] Figure 2 Shows a grayscale matrix diagram formed according to the original acoustic pulse detection data matrix according to at least one embodiment of the present application;

[0024] Figure 3 Shows a schematic diagram of the result of interpolation processing on the formed grayscale matrix diagram according to at least one embodiment of the present application;

[0025] Figure 4Shows a schematic diagram of the result of edge detection on the enhanced grayscale image matrix according to at least one embodiment of the present application;

[0026] Figure 5 Shows a scan map of an underwater target obtained by performing mapping calculation according to the relationship between the angle and the acoustic pulse detection data according to at least one embodiment of the present application;

[0027] Figure 6 Shows a block diagram of a device for processing scan sonar data according to at least one embodiment of the present application; and

[0028] Figure 7 Shows a block diagram of a device for processing scan sonar data according to at least one embodiment of the present application. Detailed implementation manners

[0029] Before proceeding with the following detailed description, it may be advantageous to set forth definitions of certain words and phrases used throughout this application. The terms "coupled", "connected" and their derivatives refer to any direct or indirect communication or connection between two or more elements, regardless of whether those elements are in physical contact with each other. The terms "transmit", "receive" and "communicate" and their derivatives cover both direct and indirect communication. The terms "include" and "comprise" and their derivatives mean including but not limited to. The term "or" is inclusive, meaning and / or. The phrases "associated with", "corresponding to" and their derivatives mean including, included within, interconnected, containing, contained within, connected or coupled to, communicating with, cooperating with, interwoven with, juxtaposed, adjacent to, bound or bound to, having, having an attribute, having a relationship or having a relationship with, etc. The term "controller" refers to any device, system or part thereof that controls at least one operation. Such a controller can be implemented in hardware, or in a combination of hardware and software and / or firmware. The functions associated with any particular controller can be centralized or distributed, whether local or remote. The phrase "at least one", when used with a list of items, means that different combinations of one or more of the listed items can be used, and it may only be necessary to have one item in the list. For example, "at least one of A, B, C" includes any one of the following combinations: A, B, C, A and B, A and C, B and C, A and B and C.

[0030] Throughout this application, definitions of other specific words and phrases are provided. Those of ordinary skill in the art should understand that in many cases, if not most cases, such definitions apply to the previous and future use of such defined words and phrases.

[0031] In this application, the application combinations of units and the hierarchical division of subunits are only for illustration. Without departing from the scope of this application, the application combinations of units and the hierarchical division of subunits can have different forms. The embodiments of this application can be implemented in different forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to make this application thorough and complete and to fully convey exemplary implementation manners to those skilled in the art. The embodiments of this application can be combined to form additional embodiments.

[0032] The following will describe this application in detail with reference to the exemplary embodiments of this application. However, this application is not limited to the embodiments described herein and can be implemented in many different forms. The described embodiments are only used to make this application thorough and complete and to fully convey the concept of this application to those skilled in the art. The features of the described embodiments can be combined with each other or replaced, unless explicitly excluded or excluded according to the context.

[0033] A mechanical scanning sonar is an active sonar. This sonar device can emit sound pulses in different directions in water and further obtain the information required for functions such as obstacle avoidance by collecting and recording the echoes reflected by targets (such as obstacles) from different directions. The ping data of the sonar refers to the return data received by the sonar device after emitting a sound pulse once. The mechanical scanning sonar is more suitable for underwater use than other devices because these common obstacle acquisition devices and detection methods may not be able to perform their functions normally underwater.

[0034] In order to enable the device to obtain as much data as possible while moving normally underwater, such as real-time extraction of the boundary information of underwater targets and comprehensive scanning and acquisition of information from multiple angles, at least one embodiment of this application proposes a method for processing scanning sonar data. This method can directly extract the boundary of underwater targets quickly based on the pulse detection data (ping data) of the mechanical scanning sonar, thereby enabling the obstacles around the device equipped with this method to be quickly identified and extracted as comprehensively and real-time as possible to ensure the safety of the device movement.

[0035] The use of mechanical scanning sonar brings many conveniences to real-time processing. The acoustic pulse detection data obtained by mechanical scanning sonar (hereinafter also simply referred to as "ping data") can be continuously collected from different angles at each moment. In addition, according to the different data collected and the underwater environment, the clutter of the underwater environment and the scanning sonar itself can be filtered by setting an adaptive threshold. The sonar scanning mode of 360 degrees continuously is called "polar coordinate scanning", and the sonar scanning mode less than 360 degrees is called "sector scanning". The ways to obtain ping data can include various ways, and the polar coordinate scanning method or the sector scanning method or other scanning methods that can obtain specific data can be adopted according to the operation requirements. Therefore, no matter which scanning method is used, usually according to the relationship between the continuous angle and the acoustic pulse detection data, the ping data can be mapped into a scanning map through mapping calculation, and then image processing is performed on the scanning map, such as extracting the boundary information of underwater targets.

[0036] However, the inventors of the embodiments of the present application noticed that directly processing the ping data matrix composed of ping data rather than the scanning map further mapped by the ping data can not only ensure real-time performance, but also facilitate some data processing to make the data more complete and consistent. In addition, the inventors also noticed that for a ping data matrix with missing data (for example, losing ping data in a short period of time), for example, the ping data at the previously collected time can be used to replace the lost ping data to ensure the integrity of the data and thus maintain the stability of the system.

[0037] The inventors of the embodiments of the present application also noticed that when using the intensity data collected by the mechanical scanning sonar to form the ping data, the peak property of the intensity data means that in the data, there are often some relatively low but still high intensity value points around the point with the highest intensity value, forming a feature like a peak. This phenomenon can better reflect the surface characteristics of the object, so as to be able to describe the surface of the object more accurately. For example, based on the peak characteristics of the intensity data, further processing can be performed on the grayscale matrix map formed by the ping data matrix, such as using interpolation and other methods. While ensuring an increase in the amount of data, it can also improve the fineness of the data, facilitating the subsequent extraction of the target boundary. In the embodiments of the present application, the fineness of the data refers to the clarity of the image. In at least one embodiment of the present application, improving the fineness of the data includes: for example, in the case of a small (sparse) amount of data, processing such as expanding and interpolating the data to increase the data; for example, in the case where the scanning sonar needs to collect parameters at a low resolution but fast to obtain the surrounding environment data in real time, in order to avoid too low clarity of the image, an interpolation operation can be performed on the data to obtain more delicate data, making the targets in the future picture clearer.

[0038] In addition, at least one embodiment of the present application further proposes to further process the enhanced grayscale matrix diagram, such as using methods such as filtering and edge detection; in addition, finally, according to the relationship between the continuous angle and the ping data, the grayscale matrix diagram is mapped into a scan diagram of the underwater target collected in real time by the scanning sonar through mapping calculation.

[0039] Figure 1 The flowchart of a method for processing scanning sonar data according to at least one embodiment of the present application is shown. As Figure 1 The method shown includes steps 101-102 described below. This method can be implemented by the device for processing scanning sonar data in the embodiments of the present application. Among them, the device can be at least one of a built-in module in the scanning sonar that can at least implement the data processing function, a data processing device independent of the sonar, and other devices in the sonar system that can be connected to the scanning sonar and process data.

[0040] In step 101, obtain the acoustic pulse detection data matrix of the underwater target collected in real time by the scanning sonar, and arrange the acoustic pulse detection data matrix in the order of continuous angles to form a grayscale matrix diagram.

[0041] In step 102, process the grayscale matrix diagram to extract the boundary of the underwater target.

[0042] For example, a mechanical scanning sonar can be used to scan the underwater environment in real time, emit acoustic pulses around the scanning sonar and receive some or all of the echoes in each emission direction. These ping data usually return in the form of a set of ping data blocks, where each ping data contains a series of sampling points, representing the single transmission and reception of the acoustic pulse, and recording the intensity or reflection situation, etc. of the acoustic pulse on the propagation path at a certain angle. The ping data at different angles can be obtained in real time to form a ping data matrix, and the ping data matrix is arranged in the order of continuous angles to form a grayscale matrix diagram. For the ping data received in real time by the mechanical scanning sonar, in the embodiments of the present application, it can be processed in real time or non-real time. For example, it can be processed after collecting a certain amount of data. The embodiments of the present application do not limit this.

[0043] For example, forming the grayscale matrix diagram may include: selecting a target intensity threshold, screening the data in the acoustic pulse detection data matrix according to the target intensity threshold, and using the screened acoustic pulse detection data matrix to form the grayscale matrix diagram.

[0044] For example, screening the data in the acoustic pulse detection data matrix according to the target intensity threshold may include: setting the data in the acoustic pulse detection data matrix that is less than the target intensity threshold to a first predetermined value.

[0045] For example, the target intensity threshold can be an adaptive intensity threshold.

[0046] For example, forming the grayscale matrix diagram may further include: setting the first n columns of the selected rows in the acoustic pulse detection data matrix to a second predetermined value to remove the noise of the scanning sonar itself, and where n may depend on the size of the scanning sonar.

[0047] For example, arranging the acoustic pulse detection data matrix in consecutive angle order may include: storing the consecutive angles and the acoustic pulse detection data corresponding to the consecutive angles in the form of key-value pairs in a dictionary.

[0048] For example, before processing the formed grayscale matrix diagram to extract the boundary of the underwater target, it may further include enhancing the formed grayscale matrix diagram to improve data fineness, and where the enhanced grayscale matrix diagram can be used to extract the boundary of the underwater target.

[0049] For example, enhancing the formed grayscale matrix diagram may include: when the acoustic pulse detection data at the current acquisition time point is not obtained, using the acoustic pulse detection data collected at the previous acquisition time point to obtain the alternative acoustic pulse detection data at the current acquisition time point.

[0050] For example, enhancing the formed grayscale matrix diagram may further include performing interpolation processing on the formed grayscale matrix diagram.

[0051] For example, before processing the formed grayscale matrix diagram to extract the boundary of the underwater target, it may further include filtering the enhanced grayscale matrix diagram.

[0052] For example, before processing the formed grayscale matrix diagram to extract the boundary of the underwater target, it may further include performing edge detection on the enhanced grayscale matrix diagram.

[0053] For example, the edge detection may include the Canny detection method, and where the upper and lower boundaries for edge detection can be determined based on the determined intensity threshold.

[0054] For example, it may further include mapping the grayscale matrix diagram to a scan diagram of the underwater target collected in real time by the scanning sonar through mapping calculation according to the relationship between the consecutive angles and the acoustic pulse detection data.

[0055] Figure 2 An example of the grayscale matrix diagram formed according to the original ping data matrix according to at least one embodiment of the present application is shown.

[0056] For example, the intensity data can be arranged in a continuous angular order to obtain a grayscale matrix. Among them, the intensity data in each ping data of the sonar can be collected in matrix form, and its form is, for example: [0,0,0,0,0,0,0,0,46,125,100,100,0,0,0,……]. The advantage of using the intensity data is that there are often some relatively low but still high intensity value points around the point with the highest intensity value. This is due to the peak nature of the detected obstacle. This phenomenon well reflects the surface characteristics of the obstacle, can better describe the surface of the obstacle, and is convenient for subsequent extraction of the obstacle boundary.

[0057] For example, for each ping data, the following formula can be used to calculate the distance from the obstacle boundary point to the scanning sonar device:

[0058]

[0059] where L represents the length of the ping data, D represents the range, i represents the obstacle boundary point, and 0 ≤ i ≤ L.

[0060] For example, the continuous scanning angles and the ping data corresponding to the angles can be stored in a dictionary in the form of key-value pairs. The dictionary list format can be as follows:

[0061] {{α 1 :{0,0,0,0,100,0,0,0,0,0……}},

[0062] {α 2 :{0,0,0,0,100,0,0,0,0,0……}},

[0063] {α 3 :{0,0,0,0,100,0,0,0,0,0……}},

[0064] {α 4 :{0,0,0,0,100,0,0,0,0,0……}},

[0065] …………………}

[0066] Adopting this storage method based on key-value pairs is more flexible than the conventional row storage method of angles and intensities, and is more suitable for unstructured data or application scenarios that require a flexible access method. For example, when the angle (key) exists and a new ping data is received, only the value (value) corresponding to the angle needs to be updated, without replacing the overall data, which is convenient for subsequent processing.

[0067] For example, in at least one example, when there is a missing ping, the data at a previous time point (such as the previous time point) can be used to supplement the current ping data to maintain the integrity of the data. The advantage of doing this is that it can locally change the data collected by the mechanical scanning sonar at the corresponding time point and process it in real time. This method based on key-value pair storage provides higher scalability and can easily add, delete, or update data. The characteristics of this data structure conform to the characteristics of mechanical scanning sonar data.

[0068] For example, in at least one example, in the step of forming a grayscale matrix, to cope with the complex and changeable underwater environment, improve the accuracy of data and the self-adaptability of the underwater environment, an adaptive intensity threshold method can be adopted. In the matrix, the data less than the intensity threshold is set to a certain predetermined value, for example, set to 0, and according to its own size (such as the scanning sonar or its platform equipment), the machine information intensity is erased, so as to avoid the situation of mis-filtering suspected obstacles due to the low overall environmental intensity. For example, in at least one example, a target intensity threshold can be selected, and the data in the ping data matrix is screened according to the target intensity threshold, and the screened ping data matrix is used to form a grayscale matrix diagram, so that some underwater clutter can be screened out.

[0069] In addition, in at least one example, since the mechanical scanning sonar scans centered on itself, the first n columns of the selected rows in the grayscale matrix can also be set to a second predetermined value to remove the noise of the scanning sonar itself. For example, to screen underwater clutter, the data in the ping data matrix less than the target intensity threshold can be set to a first predetermined value (for example, set to 0). For example, the target intensity threshold can be set to 0.2 times the maximum value in the grayscale matrix. For example, to remove its own noise, the first n columns of each row of the ping matrix can be selected for setting, where the value of n can depend on the size of the scanning sonar, such as calculating the number of selected columns through the size of its own platform equipment.

[0070] For example, an adaptive threshold can be used to screen the grayscale matrix diagram, the data of the scanning sonar itself included in the grayscale matrix diagram can be denoised, the grayscale matrix diagram can be enhanced by the method of interpolating values, the enhanced grayscale matrix diagram can be filtered and edge collected. Further, according to the relationship between the angle and the ping data, a scan diagram of the underwater target can be obtained through mapping calculation.

[0071] For example, data interpolation techniques can be used to enhance the data collected by mechanical scanning sonar. For example, taking advantage of the characteristics of dictionary storage of data, data not received at the next time point or data lost at the current time point can be filled with the data collected at the previous time point. That is, when the ping data at the current collection time point is not obtained, the ping data collected at the previous collection time point is used to obtain the alternative ping data at the current collection time point.

[0072] For example, interpolation methods can also be used to enhance the received data to improve the fineness of the data. For example, interpolation processing can be performed on the grayscale matrix diagram formed by the ping data matrix to obtain the processed grayscale matrix diagram.

[0073] The interpolation method can be implemented before processing based on the grayscale matrix diagram to extract the boundary of underwater targets. For example, it can be implemented after data acquisition and before noise filtering, or after noise filtering and before edge detection, according to the requirements of data processing. The usage scenarios include, for example, expanding data when the data volume is small, or making the data more delicate when it is necessary to collect parameters in a low-resolution but fast manner with a scanning sonar to obtain the surrounding environment data in real time. By using this comprehensive method, the data acquisition process can be effectively optimized, and the integrity and usability of the data can be improved. The interpolation method can be, for example, bilinear interpolation, which can ensure the data processing speed to meet the real-time requirement of the mechanical scanning sonar while enhancing the data.

[0074] Figure 3 A schematic diagram of the result of interpolating the formed grayscale matrix diagram according to at least one embodiment of the present application is shown.

[0075] For example, filtering techniques can be used to effectively filter out some isolated peak points with high intensity values, Gaussian noise, etc. from the matrix grayscale, making the image clearer and easier to distinguish the background from the obstacle boundary. The filtering technique can be, for example, Gaussian filtering. Gaussian filtering can smooth the image and remove noise while retaining the details and edges of the image. Among them, the convolution kernel can be set to 13×13, and the Gaussian kernel is:

[0076]

[0077] For example, edge detection methods can be used to extract edge obstacles from the grayscale matrix diagram or the enhanced grayscale matrix diagram. For example, through edge detection methods, information such as the position, shape size, and direction of the obstacle can be extracted as features of discontinuous parts, so as to facilitate subsequent decisions, such as path obstacle avoidance.

[0078] For example, edge detection can be the Canny detection method using the Canny operator. A dual-threshold setting is adopted in the Canny algorithm, that is, an upper threshold and a lower threshold are set. For the pixel points in the image that are greater than the upper threshold, they are called strong boundaries and are considered to be definitely boundaries. For the pixel points that are less than the lower threshold, they are considered to be definitely not boundaries. For the pixel points between the upper and lower thresholds, they are called weak boundaries and need to be further processed.

[0079] For example, the upper and lower boundaries of the Canny detection method adopted in at least one embodiment of the present application can be set to [0.8 × threshold, 1.2 × threshold], where the threshold can be set to the adaptive intensity threshold selected for each ping data.

[0080] Figure 4 The schematic diagram of the result of edge detection on the enhanced grayscale image matrix according to at least one embodiment of the present application is shown.

[0081] For example, after edge detection, according to the relationship between the angle and the ping data, mapping calculation can be performed to map the matrix into a scan map of a mechanical scanning sonar, and this scan map can be used for further image processing.

[0082] Figure 5 The scan map result of the underwater target obtained by mapping calculation according to the relationship between the angle and the ping data according to at least one embodiment of the present application is shown.

[0083] At least one embodiment of the present application also provides a device for processing scan sonar data.

[0084] Figure 6 The block diagram of the device for processing scan sonar data according to at least one embodiment of the present application is shown.

[0085] Refer to Figure 6 , the above device 600 includes a scan module 601 and a processing module 602.

[0086] The scan module 601 is configured to acquire the ping data matrix of the underwater target collected by the scan sonar in real time, arrange the acoustic pulse detection data matrix in the continuous angle order, and form a grayscale matrix diagram.

[0087] The processing module 602 is configured to perform processing based on the grayscale matrix diagram to extract the boundary of the underwater target.

[0088] For example, the scanning module 601 can be further configured to select a target intensity threshold, screen the data in the ping data matrix according to the target intensity threshold, and form a grayscale matrix map using the screened ping data matrix.

[0089] For example, screening the data in the ping data matrix according to the target intensity threshold can include setting the data in the ping data matrix that is less than the target intensity threshold to a first predetermined value (such as setting to 0).

[0090] For example, the target intensity threshold can be configured as an adaptive intensity threshold.

[0091] For example, forming the grayscale matrix map can further include: setting the first n columns of the selected rows in the ping data matrix to a second predetermined value to remove the noise of the scanning sonar itself, and where n can depend on the size of the scanning sonar.

[0092] For example, arranging the ping data matrix in consecutive angle order can include storing the consecutive angles and the ping data corresponding to the consecutive angles in the form of key-value pairs in a dictionary.

[0093] For example, the processing module 602 can be further configured to perform enhancement processing on the formed grayscale matrix map to improve data fineness before processing based on the grayscale matrix map to extract the boundary of the underwater target, and the enhanced grayscale matrix map is used to extract the boundary of the underwater target.

[0094] For example, performing enhancement processing on the formed grayscale matrix map can include obtaining alternative ping data at the current acquisition time point using the ping data collected at the previous acquisition time point when the ping data at the current acquisition time point is not obtained.

[0095] For example, performing enhancement processing on the formed grayscale matrix map can include performing interpolation processing on the formed grayscale matrix map.

[0096] For example, before processing based on the grayscale matrix map to extract the boundary of the underwater target, it can further include performing edge detection on the enhanced grayscale matrix map.

[0097] For example, edge detection can include the canny detection method, and where the upper and lower boundaries for edge detection can be determined based on the determined intensity threshold.

[0098] For example, the processing module 602 can also be configured to: map the grayscale matrix map to a scan map of the underwater target collected in real time by the scanning sonar through mapping calculation according to the relationship between the consecutive angles and the acoustic pulse detection data.

[0099] At least one embodiment of the present application further provides a device for processing scan sonar data, the device including at least one memory and at least one processor. The at least one memory is configured to store computer-executable instructions; the at least one processor is configured to execute the computer-executable instructions, wherein the computer-executable instructions, when executed by the at least one processor, implement the display method described in any of the foregoing embodiments. The embodiments of the present application have no limitations on the specifications, implementation manners, etc. of the memory and the processor.

[0100] At least one embodiment of the present application further provides a non-transitory storage medium that non-transitorily stores computer-executable instructions, wherein when the computer-executable instructions are executed by at least one processor, the display method described in any of the foregoing embodiments is implemented.

[0101] Figure 7 The block diagram of a device for processing scan sonar data according to at least one embodiment of the present application is shown.

[0102] Reference Figure 7 As shown in, the device 700 can be at least one of a built-in module in the scan sonar that can at least implement the data processing function, a data processing device independent of the sonar, and other devices in the sonar system that can be connected to the scan sonar and process data. The device 700 can include a processor 701, a bus 702, and a memory 703, which can be respectively used for the method for processing scan sonar data provided in any of the foregoing embodiments. Here, the memory 703 can be used to store data and / or computer code. These data include files, images, etc., and these computer codes include source programs, target programs, executable programs, etc., which can be executed by the processor to achieve the required functions. The processor 701 can be used to execute the executable program to perform related calculations. The scan sonar can be used to obtain ping data by a mechanical scan sonar. For example, the bus 702 can be used for information (data or control signals) transmission between various devices.

[0103] The processor 701 can include a central processing unit (CPU), and can further include a coprocessor as needed. The coprocessor can include a graphics processing unit (GPU) or an AI accelerator (such as a tensor processor, etc.); for example, the CPU can be a CPU based on instruction sets such as ARM, RISC-V, or X86, and can be a multi-core processor or a single-core processor, etc. The embodiments of the present application do not limit this.

[0104] The memory 703 can be a volatile memory, a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) which serves as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). It should be noted that the memory of the methods described herein is intended to include but not be limited to these and any other suitable types of memory.

[0105] Embodiments of the present application also provide a sonar system (not shown) for processing scan sonar data, including a scan sonar and a device 700 for processing scan sonar data, which can communicate with each other, for example, to transmit instructions or data. The scan sonar can include, for example, an array, an electronic cabinet, and auxiliary equipment. Among them, the array can be composed of transducers arranged in a specific manner, and the electronic cabinet can include, for example, a transmitter, a receiver, a display, a processor, and a controller, etc. The transducer can be a device for converting acoustic energy into other forms of energy, and can include devices capable of realizing technologies such as transmitting acoustic waves underwater and receiving acoustic waves underwater. The transducer for receiving can also be called a hydrophone.

[0106] The above-mentioned device may further include other functional components as needed. For example, it may further include a peripheral interface (not shown in the figure), etc.; the peripheral interface can be various types of interfaces, such as a USB interface, a Lightning interface, etc. The above-mentioned device may also include a communication device, which can communicate with a network and other devices through wireless communication. The network can be, for example, the Internet, an intranet, and / or a wireless network such as a cellular phone network, a wireless local area network (LAN), and / or a metropolitan area network (MAN). For example, wireless communication can use any one of a variety of communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi (e.g., based on IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n standards), Voice over Internet Protocol (VoIP), WiMAX, protocols for email, instant messaging, and / or Short Message Service (SMS), or any other suitable communication protocol.

[0107] Those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and steps described in this application can be implemented as hardware, software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in terms of their functional sets. Whether such a functional set is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functional sets in different ways for each specific application, but such design decisions should not be construed as causing a departure from the scope of this application.

[0108] The various illustrative logical blocks, modules, and circuits described in this application can be implemented or executed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor can be a microprocessor, but in an alternative, the processor can be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors collaborating with a DSP core, or any other such configuration.

[0109] The steps of the methods or algorithms described in this application can be embodied directly in hardware, in software modules executed by a processor, or in a combination of the two. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read from and write to the storage medium. In an alternative, the storage medium can be integrated into the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In an alternative, the processor and the storage medium can reside in the user terminal as discrete components.

[0110] In one or more exemplary designs, the functions can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored on or transmitted via a computer-readable medium as one or more instructions or code. The computer-readable medium includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

[0111] The above embodiments of this application are merely for easy description and to assist in a comprehensive understanding of this application, and are not intended to limit the scope of this application. Therefore, it should be understood that all modifications and changes or forms of modifications and changes originating from the technical concept of this application, in addition to the above embodiments disclosed herein, fall within the scope of this application.

Claims

1. A method for processing scanning sonar data, comprising: Acquire the acoustic pulse detection data matrix of the underwater target collected by the scanning sonar in real time, and arrange the acoustic pulse detection data matrix in a continuous angle sequence in real time to form a grayscale matrix diagram; as well as Processing is performed based on the grayscale matrix image to extract the boundary of the underwater target, Wherein, arranging the acoustic pulse detection data matrix according to the continuous angle sequence includes: The continuous angles and the acoustic pulse detection data corresponding to the continuous angles are stored in a dictionary in the form of key-value pairs, Wherein, before the processing based on the grayscale matrix image is performed to extract the boundary of the underwater target, the method also includes: when the acoustic pulse detection data at the current acquisition time point is not obtained, using the acoustic pulse detection data collected at the previous acquisition time point to obtain alternative acoustic pulse detection data at the current acquisition time point.

2. The method according to claim 1, wherein: The forming of the grayscale matrix diagram comprises: Select the target intensity threshold, The data in the acoustic pulse detection data matrix is ​​screened according to the target intensity threshold, and the screened acoustic pulse detection data matrix is ​​used to form the grayscale matrix diagram.

3. The method according to claim 2, wherein: Screening the data in the acoustic pulse detection data matrix according to the target intensity threshold comprises: The data in the acoustic pulse detection data matrix that is smaller than the target intensity threshold is set as a first predetermined value.

4. The method according to claim 2, wherein: The target intensity threshold is an adaptive intensity threshold.

5. The method according to claim 2, wherein: The forming of the grayscale matrix diagram further comprises: removing the noise of the scanning sonar itself by setting the first n columns of the selected row in the acoustic pulse detection data matrix to a second predetermined value, and Wherein, the n depends on the size of the scanning sonar.

6. The method according to any one of claims 1 to 5, before said processing based on said grayscale matrix image to extract the boundary of said underwater target, further comprising: The formed grayscale matrix image is enhanced to improve data refinement, wherein the enhanced grayscale matrix image is used to extract the boundary of the underwater target.

7. The method according to claim 6, wherein: Performing enhancement processing on the formed grayscale matrix image, including: The formed grayscale matrix image is interpolated.

8. The method according to claim 6, before said processing based on said gray matrix image to extract the boundary of said underwater target, further comprising: Perform filtering on the enhanced grayscale matrix image.

9. The method according to claim 6, before said processing based on said grayscale matrix image to extract the boundary of said underwater target, further comprising: Perform edge detection on the enhanced grayscale matrix image.

10. The method according to claim 9, wherein: The edge detection includes a Canny detection method, and Therein, upper and lower boundaries for edge detection are determined based on the determined intensity threshold.

11. The method according to any one of claims 1 to 5, further comprising: According to the relationship between the continuous angles and the acoustic pulse detection data, the grayscale matrix image is mapped into a scanning image of the underwater target collected in real time by the scanning sonar through mapping calculation.

12. An apparatus for processing scanning sonar data, comprising: A scanning module is configured to obtain a sound pulse detection data matrix of underwater targets collected by the scanning sonar in real time, and to arrange the sound pulse detection data matrix in a continuous angle sequence in real time to form a grayscale matrix diagram; as well as a processing module, configured to perform processing based on the grayscale matrix image to extract the boundary of the underwater target, The scanning module is further configured to store the continuous angles and the acoustic pulse detection data corresponding to the continuous angles in a dictionary in the form of key-value pairs. Wherein, the processing module is also configured to, before processing based on the grayscale matrix image to extract the boundary of the underwater target, use the acoustic pulse detection data collected at the previous acquisition time point to obtain alternative acoustic pulse detection data for the current acquisition time point when the acoustic pulse detection data for the current acquisition time point has not been obtained.

13. An apparatus for processing data from a scanning sonar, comprising: at least one processor; at least one memory having computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the method of any one of claims 1 to 11 is performed.

14. A non-transitory storage medium storing executable instructions, in, When the executable instructions are executed by at least one processor, the method of any one of claims 1 to 11 is performed.

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