Target detection method and system based on Gm-APD range image and intensity image

Abstract

The application provides a Gm-APD distance image and intensity image target detection method and system, and belongs to the image recognition technical field, and specifically comprises the following steps: acquiring a distance image and an intensity image of a remote unmanned aerial vehicle target detected by a Gm-APD array; selecting an initial seed point in the distance image based on a preset seed point selection rule; setting a region growing rule, adding pixel points meeting the region growing rule in the distance image to a growing queue, and performing region growing; when a growing stop condition is reached, the region growing is stopped; calculating the area of the grown region; when the area of the region is within a preset range, the region is an unmanned aerial vehicle target region; and based on the distance image and the intensity image corresponding to the unmanned aerial vehicle target region, the contour of the unmanned aerial vehicle target is reconstructed. The application scheme can retain good boundary information and segmentation results, is rigorous and conforms to the characteristics of the distance image and the intensity image, can flexibly adjust parameters according to the image characteristics, and adaptively performs segmentation.

Description

Technical Field

[0001] The present application relates to the field of image recognition technology, and in particular to a target detection method and system based on Gm-APD range image and intensity image. Background Art

[0002] In recent years, with the increasing maturity of unmanned aerial vehicle (UAV) technology, its application areas have gradually expanded. For example, in the military, it is used for aerial deployment to reduce pilot casualties; in the civilian and commercial sectors, it can be used for cargo transportation, emergency search and rescue, and as a relay system. However, the management of UAVs remains imperfect, and most of them fly illegally, posing a serious threat to citizens' privacy and the safety of specific locations. How to effectively detect and warn of such targets has become a hot topic. However, due to the small size of UAVs and the large search space, detecting UAVs is not easy. Compared to daytime conditions, the lack of lighting information at night makes this problem even more difficult.

[0003] Traditional lidar can detect high-speed moving targets at long distances, but is unable to detect small targets at relatively close range. A proposed lidar system, utilizing Geiger-mode avalanche photodiodes (Gm-APDs) as detectors and photon counting technology, can easily detect weak light signals. However, the range and intensity images detected by the Gm-APD array differ significantly from those of traditional radar: first, the imaging method differs, and second, the noise generation differs. The range image detected by the Gm-APD array contains a large amount of lost information and randomly distributed, irregular range anomaly noise. Furthermore, the image resolution of a small drone target detected 5 kilometers away using a 64×64 Gm-APD array in this system is too low, making it difficult to distinguish and recover many details.

[0004] Internationally, research into processing range and intensity images using Gm-APD arrays has been underway for a while now, while China, while relatively new, has seen a surge in interest. Significant investment has been made in algorithm refinement and optimization, resulting in consistently improved results. Existing range and intensity image processing techniques, while performing denoising, can compromise target details. Therefore, finding a noise suppression algorithm that effectively improves noise suppression while preserving target details remains a key research challenge. Summary of the Invention

[0005] In view of this, an embodiment of the present application provides a target detection method and system based on Gm-APD range image and intensity image, which at least partially solves the problem that the existing range image and intensity image processing technology, while performing denoising, will damage the detailed information of the target.

[0006] In a first aspect, an embodiment of the present application provides a target detection method based on Gm-APD range image and intensity image, the method comprising:

[0007] Obtain the range image and intensity image of the long-range UAV target detected by the Gm-APD array;

[0008] Selecting an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point;

[0009] Setting a region growing rule, adding pixels in the range image that meet the region growing rule to a growing queue, performing region growing, and stopping region growing when a growth stop condition is met;

[0010] Calculate the area of ​​the grown region. If the area is within the preset range, the region is considered to be the target area of ​​the drone.

[0011] Based on the range image and the intensity image corresponding to the drone target area, the outline of the drone target is reconstructed.

[0012] According to a specific implementation of the embodiment of the present application, the preset seed point selection rule includes:

[0013] The pixel grayscale value gradient of every two pixels in the range image is calculated, and the pixel with the largest gradient amplitude is indexed as the initial seed point.

[0014] According to a specific implementation of the embodiment of the present application, the preset seed point selection rule further includes:

[0015] There are at least a preset number of pixels of the same nature around the initial seed point, and the pixel grayscale value of the initial seed point is not zero;

[0016] When the number of pixels of the same nature around the initial seed point does not meet the preset number requirement, or the pixel grayscale value of the initial seed point is zero, the pixel with the second largest index gradient amplitude is selected as the initial seed point;

[0017] This process is repeated until the best initial seed point is found.

[0018] According to a specific implementation of the embodiment of the present application, the preset number is set to 4.

[0019] According to a specific implementation of the embodiment of the present application, the region growing rule is set as:

[0020] Pixels in the range image whose pixel grayscale values ​​differ from those of the initial seed point by less than a preset threshold are added to the growth queue.

[0021] According to a specific implementation of the embodiment of the present application, the preset threshold is set to 10.

[0022] According to a specific implementation of the embodiment of the present application, when the growth stop condition is reached, stopping the regional growth includes:

[0023] Each pixel point in the growth queue is used as a seed. With each seed as the center, the pixel points around each seed are visited, and the pixel points that meet the regional growth rules are continuously added to the growth queue. At the same time, the visited seeds in the growth queue are marked until all seeds in the growth queue are marked, and then the growth is stopped.

[0024] According to a specific implementation of the embodiment of the present application, the preset range is set to 4 to 500.

[0025] According to a specific implementation of an embodiment of the present application, the method for selecting the initial seed point includes a human-computer interaction method and an algorithm-independent method.

[0026] In a second aspect, an embodiment of the present application further provides a target detection system based on Gm-APD range image and intensity image, the system comprising:

[0027] The acquisition module is used to obtain the range image and intensity image of the long-range UAV target detected by the Gm-APD array;

[0028] A selection module, configured to select an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point;

[0029] A region growing module is used to set a region growing rule, add pixels in the range image that meet the region growing rule to a growth queue, perform region growing, and stop region growing when a growth stop condition is met;

[0030] A calculation module is used to calculate the area of ​​the grown region. When the area of ​​the region is within a preset range, the region is determined to be the target area of ​​the drone;

[0031] The contour reconstruction module is used to reconstruct the contour of the drone target based on the range image and the intensity image corresponding to the drone target area.

[0032] Beneficial effects:

[0033] The target detection method and system based on Gm-APD range image and intensity image in the embodiments of the present application process the range image and intensity image obtained by single-photon detection based on an improved region growing algorithm, which can retain good boundary information and segmentation results, and does not require pre-processing of the image or providing prior knowledge. It only needs to clarify the rules for seed point selection to perform segmentation, effectively improving the noise suppression capability without compromising the ability to protect target detail information.

[0034] The improved region growing algorithm proposed in the present invention strictly conforms to the characteristics of distance image and intensity image, performs seed point selection and setting of growth rules, and can propose corresponding solutions according to actual needs to solve actual needs.

[0035] The present invention only processes the detected range image and can simultaneously solve the problem of extracting corresponding data of the intensity image. The algorithm is simple and easy to understand, and can flexibly adjust parameters according to image characteristics to perform segmentation adaptively. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] Figure 1 Flowchart of a target detection method based on Gm-APD range image and intensity image according to one embodiment of the present invention;

[0038] Figure 2 1. A typical structural diagram of a range image and an intensity image of a UAV according to an embodiment of the present invention;

[0039] Figure 3 A diagram showing a region growing principle according to an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of a range image and an intensity image of a high-rise building according to an embodiment of the present invention;

[0041] Figure 5 FIG. 4 is a structural block diagram of a target detection system based on Gm-APD range image and intensity image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0043] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0044] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0045] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0046] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described may be practiced without these specific details.

[0047] In the first aspect, the embodiment of the present application provides a target detection method based on Gm-APD range image and intensity image, as shown below. Figures 1 to 5 Provide a detailed description.

[0048] This application uses the image characteristics of the range image and intensity image of a long-range UAV target detected by a 64*64Gm-APD array to propose an improved region growing algorithm for target recognition. The typical structure of the 64*64 UAV range image and intensity image detected by the Gm-APD array is as follows: Figure 2 shown.

[0049] In one embodiment, referring to Figure 1 , based on the Gm-APD range image and intensity image target detection method, specifically including the following steps:

[0050] Step S101: Acquire the range image and intensity image of the long-distance UAV target detected by the Gm-APD array;

[0051] Step S102: selecting an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point;

[0052] Step S103: setting a region growing rule, adding pixels in the range image that meet the region growing rule to a growing queue, performing region growing, and stopping region growing when a growth stop condition is met;

[0053] Step S104: Calculate the area of ​​the grown region. If the area of ​​the region is within a preset range, the region is determined to be the target area of ​​the drone.

[0054] Step S105 : reconstructing the outline of the drone target based on the range image and the intensity image corresponding to the drone target area.

[0055] Region growing is a method that uses data characteristics as a judgment criterion, compares the characteristics of any point with the surrounding points, merges data points with similar properties, and then realizes region recognition and segmentation. For each region, a seed point is first designated as the starting point for growth, and then the pixels in the area around the seed point are compared with the seed point, and points with similar properties are merged and continue to grow outward until no pixels that meet the conditions are included. The principle diagram of region growing is as follows: Figure 3 shown.

[0056] The current original region growing algorithm has problems such as low efficiency, over-segmentation and arbitrary seed point selection when performing region segmentation. Therefore, this application improves the region growing algorithm by optimizing the seed point selection rules.

[0057] The range image of a long-range drone target detected by the Gm-APD array shows that the distance values ​​of the target area are similar, with a range of no more than 10, and essentially form a closed area. The distance values ​​of the target area differ significantly from the background noise, reaching over 100. However, the intensity difference between the background noise and the target area boundary in the intensity image is close, making it impossible to segment the target area using a threshold. Comparing the range and intensity images based solely on their composition reveals that the range and intensity images formed by both the area target and the background noise differ only in numerical value, but the distance and intensity images formed at the same time are located in the same position.

[0058] Based on the above analysis of the range image and intensity image of general UAV, a region growing algorithm based on the optimized seed point selection rule is proposed.

[0059] In one embodiment, the preset seed point selection rule includes:

[0060] The pixel grayscale value gradient of every two pixels in the range image is calculated, and the pixel with the largest gradient amplitude is indexed as the initial seed point.

[0061] The preset seed point selection rules also include:

[0062] There are at least a preset number of pixels of the same nature around the initial seed point, and the pixel grayscale value of the initial seed point is not zero;

[0063] When the number of pixels of the same nature around the initial seed point does not meet the preset number requirement, or the pixel grayscale value of the initial seed point is zero, the pixel with the second largest index gradient amplitude is selected as the initial seed point;

[0064] This process is repeated until the best initial seed point is found.

[0065] In specific implementation, the preset seed point selection process specifically includes the following steps:

[0066] a) Based on the difference in pixel values ​​between the background noise and the target, the gradient of the 64*64 matrix is ​​first calculated. The pixel with the largest gradient amplitude is indexed as the initial seed element. The initial seed point is intended to be the critical point between the target area and the noise point.

[0067] b) The initial seed element constructed based on the background noise and the target area must meet the following requirements: there are at least four pixels with the same properties around the initial seed element and the grayscale value of the initial seed pixel is not zero;

[0068] c) If the initial seed point does not meet the condition in b), the initial seed point is set to the pixel point where the matrix gradient amplitude has the second largest value;

[0069] Repeat steps b) and c) until the best initial seed point is found.

[0070] In one embodiment, the preset number is set to 4.

[0071] In one embodiment, the region growing rule is set as:

[0072] Pixels in the range image whose pixel grayscale values ​​differ from those of the initial seed point by less than a preset threshold are added to the growth queue.

[0073] Furthermore, the preset threshold is set to 10.

[0074] In specific implementation, for the growth criterion: based on the analysis of a large amount of noisy range image data, the grayscale value difference between the target area and the background noise can reach more than 10, while the grayscale value difference inside the target area is small. Therefore, the judgment of the growable seeds in the growth process is set to: the difference in the grayscale value of the pixel with the initial seed point, that is, if the grayscale difference is less than the threshold, the adjacent pixel is added to the growth queue.

[0075] In one embodiment, when the growth stop condition is reached, stopping the region growth includes:

[0076] Each pixel point in the growth queue is used as a seed. With each seed as the center, the pixel points around each seed are visited, and the pixel points that meet the regional growth rules are continuously added to the growth queue. At the same time, the visited seeds in the growth queue are marked until all seeds in the growth queue are marked, and then the growth is stopped.

[0077] In the specific implementation, for the growth stopping condition: first traverse the pixel points around the initial seed point, and add the pixel points that meet the growth criteria to the growth queue. Next, take each seed in the growth queue as the center, visit the surrounding pixel points, continue to add the seeds that meet the growth criteria to the growth queue, and mark the visited seeds in the growth queue at the same time, until all seeds in the growth queue are marked, that is, growth stops.

[0078] The above data processing based on the improved region growing algorithm only performs preliminary denoising. The detected range image and intensity image may contain other targets, so the detection of drone targets still requires identification. In this application, a method based on a fixed threshold is used to determine the presence or absence of drone targets.

[0079] Consider that the segmented area may be a noise connected area and other non-UAV target areas, such as Figure 4 Shown are the range image and intensity image of the detected high-rise building.

[0080] In implementation, we first calculate the area of ​​the grown region and then set a threshold for the area. We set a fixed threshold based on extensive experimental data. It's known that noise pixels are scattered and small, while the pixels formed by tall buildings are much larger than those formed by drones, typically reaching over a hundred pixels. Based on the above analysis of the target's connected area, and for a 64*64 image, the maximum number of pixels in a region is 4096, we set the maximum area threshold for drone targets in the range image to no more than 500, and the minimum area threshold to no less than 4.

[0081] In one embodiment, the preset range is set to 4-500.

[0082] In one embodiment, the initial seed point selection method includes a human-computer interaction method and an algorithm autonomous method.

[0083] Regarding the selection of seed points, two methods are generally used: human-computer interaction and algorithm autonomy. However, in order to reduce the influence of subjective factors, algorithm autonomy is currently used more often.

[0084] In the second aspect, the embodiment of the present application also provides a target detection system based on Gm-APD range image and intensity image, referring to Figure 5 , the system comprising:

[0085] An acquisition module 501 is used to acquire a range image and an intensity image of a long-range UAV target detected by the Gm-APD array;

[0086] A selection module 502 is configured to select an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point;

[0087] The region growing module 503 is configured to set a region growing rule, add pixels in the range image that meet the region growing rule to a growing queue, perform region growing, and stop region growing when a growth stop condition is met;

[0088] The calculation module 504 is used to calculate the area of ​​the grown region. When the area of ​​the region is within a preset range, the region is determined to be the target area of ​​the drone.

[0089] The contour reconstruction module 505 is configured to reconstruct the contour of the drone target based on the range image and the intensity image corresponding to the drone target area.

[0090] In one embodiment, the selection module 502 is further configured to:

[0091] The pixel grayscale value gradient of every two pixels in the range image is calculated, and the pixel with the largest gradient amplitude is indexed as the initial seed point.

[0092] In one embodiment, the selection module 502 is further configured to:

[0093] There are at least a preset number of pixels of the same nature around the initial seed point, and the pixel grayscale value of the initial seed point is not zero;

[0094] When the number of pixels of the same nature around the initial seed point does not meet the preset number requirement, or the pixel grayscale value of the initial seed point is zero, the pixel with the second largest index gradient amplitude is selected as the initial seed point;

[0095] This process is repeated until the best initial seed point is found.

[0096] In one embodiment, the selection module 502 is further configured to: set the preset number to 4.

[0097] In one embodiment, the region growing module 503 is further configured to:

[0098] Pixels in the range image whose pixel grayscale values ​​differ from those of the initial seed point by less than a preset threshold are added to the growth queue.

[0099] In one embodiment, the region growing module 503 is further configured to: set the preset threshold to 10.

[0100] In one embodiment, the region growing module 503 is further configured to:

[0101] Each pixel point in the growth queue is used as a seed. With each seed as the center, the pixel points around each seed are visited, and the pixel points that meet the regional growth rules are continuously added to the growth queue. At the same time, the visited seeds in the growth queue are marked until all seeds in the growth queue are marked, and then the growth is stopped.

[0102] In one embodiment, the calculation module 504 is further configured to set the preset range to 4-500.

[0103] In one embodiment, the selection module 502 is further configured to select the initial seed point in a manner including a human-computer interaction manner and an algorithm-independent manner.

[0104] The embodiment provided by the present invention proposes a long-range small target detection system to address the hazards brought by drones. It uses a 64*64Gm-APD array detection to detect long-range drone targets and can locate drone targets.

[0105] The actual target range image and intensity image obtained by the 64*64Gm-APD array detection system are characterized by random distribution of background noise, low resolution of the image detected for small targets at long distances, and less target detail information that is difficult to recover.

[0106] Based on the analysis of the actual target range image and intensity image characteristics, an improved region growing algorithm is proposed to process the original range image, setting the noise points as non-target parts instead of simply performing denoising. The presence of the target is detected by further determining the area of ​​the growing region.

[0107] The present invention only preprocesses the actually collected single-frame target range image and intensity image, and detects the presence or absence of the target based on the preprocessing. This invention provides good sample data for subsequent joint target recognition between multiple frames.

[0108] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A target detection method based on Gm-APD range image and intensity image, characterized in that: The method comprises: Obtain the range image and intensity image of the long-range UAV target detected by the Gm-APD array; Selecting an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point; Setting a region growing rule, adding pixels in the range image that meet the region growing rule to a growing queue, performing region growing, and stopping region growing when a growth stop condition is met; Calculate the area of ​​the grown region. If the area is within the preset range, the region is considered to be the target area of ​​the drone. Based on the range image and the intensity image corresponding to the drone target area, the outline of the drone target is reconstructed.

2. The target detection method based on Gm-APD range image and intensity image according to claim 1, characterized in that: The preset seed point selection rules include: The pixel grayscale value gradient of every two pixels in the range image is calculated, and the pixel with the largest gradient amplitude is indexed as the initial seed point.

3. The target detection method based on Gm-APD range image and intensity image according to claim 2, characterized in that: The preset seed point selection rules also include: There are at least a preset number of pixels of the same nature around the initial seed point, and the pixel grayscale value of the initial seed point is not zero; When the number of pixels of the same nature around the initial seed point does not meet the preset number requirement, or the pixel grayscale value of the initial seed point is zero, the pixel with the second largest index gradient amplitude is selected as the initial seed point; This process is repeated until the best initial seed point is found.

4. The target detection method based on Gm-APD range image and intensity image according to claim 3, characterized in that: The preset number is set to 4.

5. The target detection method based on Gm-APD range image and intensity image according to claim 1, characterized in that: The region growing rule is set as: Pixels in the range image whose pixel grayscale values ​​differ from those of the initial seed point by less than a preset threshold are added to the growth queue.

6. The target detection method based on Gm-APD range image and intensity image according to claim 5, characterized in that: The preset threshold is set to 10.

7. The target detection method based on Gm-APD range image and intensity image according to claim 1, characterized in that: When the growth stopping condition is reached, stopping the regional growth comprises: Each pixel point in the growth queue is used as a seed. With each seed as the center, the pixel points around each seed are visited, and the pixel points that meet the regional growth rules are continuously added to the growth queue. At the same time, the visited seeds in the growth queue are marked until all seeds in the growth queue are marked, and then the growth is stopped.

8. The target detection method based on Gm-APD range image and intensity image according to claim 1, characterized in that: The preset range is set to 4 to 500.

9. The target detection method based on Gm-APD range image and intensity image according to claim 1, characterized in that: The initial seed point selection method includes a human-computer interaction method and an algorithm autonomous method.

10. A target detection system based on Gm-APD range image and intensity image, characterized in that: The system comprises: The acquisition module is used to obtain the range image and intensity image of the long-range UAV target detected by the Gm-APD array; A selection module, configured to select an initial seed point in the range image based on a preset seed point selection rule, wherein the initial seed point serves as a critical point between the drone target area and the noise point; A region growing module is used to set a region growing rule, add pixels in the range image that meet the region growing rule to a growth queue, perform region growing, and stop region growing when a growth stop condition is met; A calculation module is used to calculate the area of ​​the grown region. When the area of ​​the region is within a preset range, the region is determined to be the target area of ​​the drone; The contour reconstruction module is used to reconstruct the contour of the drone target based on the range image and the intensity image corresponding to the drone target area.

Images