A target positioning method applied to lobster-eye optical detection system

Abstract

The present invention provides a target positioning method applied to a lobster-eye optical detection system. Starting from image characteristics, the target positioning problem of the lobster-eye optical system is converted into a key point detection problem of a cross-focal spot. The positioning idea of ​​first screening the key area and then extracting the target source point is proposed. The calculation principle of the target azimuth angle is explored through optical path analysis, and the acquisition of precise target position information including position coordinates and relative azimuth angle is gradually achieved. This method is more in line with the needs of optical detection, reduces the missed detection and false detection rates caused by the interference of the cross-focal spot shape and its interlaced, disordered and mutually occluded cross-focal spot, and achieves better detection and positioning effects.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel photoelectric system detection and target identification and tracking, and in particular relates to a target positioning method applied to a lobster eye optical detection system. Background Art

[0002] Optical detection systems collect, process, and analyze light signals to detect, identify, and monitor objects and environments. Due to their ease of deployment, wide application range, and ability to provide intuitive and detailed information, they have become a key tool for precise perception and intelligent decision-making in a variety of fields. In modern high-tech warfare, they are an indispensable means of obtaining battlefield situational information. In civilian applications, they have also been widely used in various fields, including social security, resource exploration, remote sensing navigation, and environmental protection.

[0003] To meet complex detection needs, optical detection systems have high requirements for working distance, field of view, and image clarity. The lobster-eye optical structure is characterized by strong adaptability, high directional sensitivity, and a large working field of view. Compared with traditional optical detection systems, optical detection systems using the lobster-eye structure are small in size, low in cost, and easy to deploy. They have great application potential in aerial remote sensing, large-scale monitoring and warning, search and navigation, and other fields. However, the point spread function of the lobster-eye optical system is relatively unique, and the image formed has a significant cross feature. Therefore, it is necessary to obtain the position and azimuth information of the target through the cross focal spot formed by the target to achieve the detection purpose.

[0004] In early studies, Zhan et al. proposed an image enhancement and region of interest extraction method for lobster-eye optical images, which consists of three modules: "de-gridding", "low-brightness image reconstruction" and "region of interest (ROI) extraction", providing a new method for the field of lobster-eye image processing; Sawano et al. proposed a lobster-eye source detection algorithm based on one-dimensional projection, which greatly optimized the source detection effect through double condition screening; Liu et al. proposed a lobster-eye telescope target detection idea based on machine learning algorithm, advocating the combination of multiple image processing methods to construct a lobster-eye target detection framework, and compared and analyzed the performance of different methods; subsequently, Liu et al. improved the above research and proposed a lobster-eye telescope target detection pipeline algorithm framework based on machine learning algorithm, dividing the source detection of celestial targets into three processes: super-bright, medium-brightness and tiny-brightness detection, and applying different schemes respectively.

[0005] In other words, current target localization methods based on lobster-eye optical systems still have numerous shortcomings: 1. The algorithm is susceptible to interference from the cross shape, resulting in poor detection and a high false positive rate. 2. It can only determine the target range and approximate location, but cannot precisely locate the target's source. 3. It is limited to target position information and lacks target azimuth determination. 4. It is difficult to resolve the problem of cluttered and obstructed cross-focus spots.

[0006] It can be seen that there is currently little research on image processing methods for lobster eye target positioning. Not only does it face the problem of the lobster eye's cross focal spots being intertwined and blocking each other, resulting in poor detection effect, but it also cannot meet the requirements of accurate positioning of the target source point of the lobster eye optical detection system, greatly limiting its wide application in detection and warning fields. Summary of the Invention

[0007] In order to solve the problems that the target recognition method of lobster eye images in the existing technology cannot solve the problems of cross focal spots with messy distribution and mutual occlusion, accurate positioning of target source points and target orientation judgment, the present invention provides a target positioning method applied to the lobster eye optical detection system. First, the key area is screened, and then the positioning idea of ​​the target point light source is extracted. It can effectively solve the problem of false detection caused by target aliasing. The recognition and positioning algorithm has high accuracy and good detection and positioning effect.

[0008] A target positioning method applied to a lobster-eye optical detection system comprises the following steps:

[0009] A deep learning-based target detection model is used to extract the key areas of the cross-focus spot of each point light source in the target image acquired by the lobster-eye optical detection system.

[0010] The centroid of each point light source in the cross focus key area is used as the position coordinate of each point light source on the target image;

[0011] Obtaining the image height of each point light source on the target image according to the position coordinates of each point light source on the target image;

[0012] The azimuth angle of each point light source relative to the lobster-eye optical detection system is obtained based on the image height of each point light source on the target image. The calculation method of the azimuth angle ω' corresponding to any point light source is: Where h is the image height and R is the curvature radius of the lobster-eye optical detection system.

[0013] Furthermore, the image height h of any point light source on the target image is obtained as follows:

[0014] h=KL

[0015]

[0016] Among them, K is the pixel size of the detector receiving image in the lobster eye optical detection system, and L is the distance between the position coordinates (xi, yi) of the current point light source on the target image and the geometric center point (x0, y0) of the detector receiving image.

[0017] Furthermore, the target detection model is a YOLOv5s target detection model, and the YOLOv5s target detection model includes a CSPDarknet53 structure backbone network, a feature pyramid network, a path aggregation network, a detection head, and a non-maximum suppression network;

[0018] The method for obtaining the key area of ​​the cross focus of any point light source is as follows:

[0019] The CSPDarknet53 structure backbone network of the YOLOv5s target detection model performs initial feature extraction on the target image;

[0020] The feature pyramid network and path aggregation network are used in sequence to fuse the extracted initial features at different levels to obtain fused features;

[0021] The detection head in the YOLOv5s target detection model predicts the target bounding box of the fused features, and obtains the target bounding box corresponding to each point light source and the point light source category score of each target bounding box;

[0022] The category corresponding to the maximum score of the point light source category of each target bounding box is taken as the point light source category of each target bounding box;

[0023] The non-maximum suppression network is used to remove the overlapping target bounding boxes belonging to the same point light source category in each target bounding box. For each point light source, only the target bounding box corresponding to the maximum score of the point light source category is retained to obtain the cross focus key area corresponding to each point light source in the target image.

[0024] Furthermore, the method for obtaining the position coordinates of each point light source on the target image is:

[0025] Gaussian filtering is performed on the key area of ​​the cross-focus spot of each point light source to obtain a filtered image that removes scattered noise and highlights the central bright spot;

[0026] Perform an erosion operation on the filtered image to obtain a connected image with disconnected areas removed;

[0027] Binarize the connected image according to the set threshold to obtain a binary image that highlights the candidate areas of each point light source;

[0028] Perform a dilation operation on the binary image to obtain a dilated image that merges isolated bright spots;

[0029] Perform contour detection on the expanded image to obtain the contours of each point light source;

[0030] The centroid of each contour is calculated by the zero-order moment method, and the centroid of each contour is used as the position coordinate of the point light source corresponding to each contour on the target image.

[0031] Furthermore, the deep learning-based target detection model is a YOLOv5s target detection model, a YOLOv5x target detection model, a YOLOv8s target detection model, or a YOLOv8n target detection model.

[0032] Furthermore, the lobster eye optical detection system has a working distance of 2 km and a working field angle of 42°.

[0033] Furthermore, a target positioning method applied to the lobster eye optical detection system further includes the following steps:

[0034] A rectangular frame is used to select and a solid circle is used to mark each point light source on the target image. At the same time, additional data labels (xi, yi, ω') are displayed around the rectangular frame of each point light source on the target image.

[0035] Beneficial effects:

[0036] 1. The present invention provides a target positioning method applied to a lobster-eye optical detection system. Starting from image characteristics, the target positioning problem of the lobster-eye optical system is transformed into a key point detection problem of a cross-focus spot. A positioning idea of ​​first screening the key area and then extracting the target source point is proposed. The calculation principle of the target azimuth angle is explored through optical path analysis, and the precise position information of the target, including position coordinates and relative azimuth angle, is gradually acquired. This method is more in line with the needs of optical detection, reduces the missed detection and false detection rates caused by the interference of the cross-focus spot shape and its interlaced, disordered and mutually occluded cross-focus spots, and achieves better detection and positioning effects.

[0037] 2. The present invention provides a target positioning method applied to the lobster eye optical detection system, which uses a series of processing methods such as morphological operations and contour detection, with small calculation amount and fast calculation speed.

[0038] 3. The present invention provides a target positioning method applied to the Lobster Eye optical detection system. The YOLOv5s algorithm model is selected to detect the key area of ​​the cross focus spot. The detection efficiency is high, the algorithm volume is small, and the detection efficiency is high. It can better meet the lightweight and real-time requirements of the supporting algorithm of the Lobster Eye optical detection system deployed on the mobile terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the target positioning method of the lobster eye optical detection system described in the present invention.

[0040] Figure 2 This is a flow chart of the target source point positioning module described in the present invention.

[0041] Figure 3 This is a diagram of the focusing principle of the lobster eye optical system described in the present invention.

[0042] Figure 4 This is a schematic diagram of the principle of focusing parallel light incident on the lobster-eye optical system of the present invention.

[0043] Figure 5 This is an imaging simulation diagram of the lobster eye optical detection system in the optical simulation analysis software described in the present invention.

[0044] Figure 6 This is the final effect diagram of the target positioning method of the lobster eye optical detection system described in the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0046] In view of the many problems existing in the existing lobster-eye optical system target detection technology, the main purpose of the present invention is to provide a lobster-eye optical detection system target positioning method that integrates the YOLOv5 target detection model with processing methods such as morphological operations and can simultaneously obtain comprehensive information of target position and azimuth.

[0047] Based on the characteristics of lobster-eye images, the present invention proposes a target positioning algorithm process design concept suitable for the lobster-eye optical detection system. It can solve various problems such as the mutual occlusion of cross-focus spots in lobster-eye images, the precise positioning of target sources, and the judgment of the azimuth angle of the target relative to the detection system, and obtain the precise position information of each target within the detection range.

[0048] like Figure 1 As shown, the present invention provides a target positioning method applied to a lobster eye optical detection system, characterized in that it includes the following steps:

[0049] S1: A deep learning-based target detection model is used to extract the key areas of the cross-focus spot of each point light source in the target image acquired by the lobster-eye optical detection system;

[0050] The following describes the process of obtaining the cross-focus key area of ​​each point light source, taking the YOLOv5s target detection model as an example. The YOLOv5s target detection model includes a CSPDarknet53 structure backbone network, a feature pyramid network, a path aggregation network, a detection head, and a non-maximum suppression network. The method for obtaining the cross-focus key area of ​​any point light source is as follows:

[0051] S11: The CSPDarknet53 backbone network of the YOLOv5s target detection model performs initial feature extraction on the target image;

[0052] S12: Use the feature pyramid network and path aggregation network in sequence to fuse the extracted initial features at different levels to obtain fused features;

[0053] S13: Use the detection head in the YOLOv5s target detection model to predict the target bounding box of the fused features, and obtain the target bounding box corresponding to each point light source and the point light source category score of each target bounding box;

[0054] S14: taking the category corresponding to the maximum score of the point light source category of each target bounding box as the point light source category of each target bounding box;

[0055] S15: A non-maximum suppression network is used to remove overlapping target bounding boxes belonging to the same point light source category in each target bounding box, and only the target bounding box corresponding to the maximum score of the point light source category is retained for each point light source, thereby obtaining the cross focus key area corresponding to each point light source in the target image.

[0056] In other words, the cross-focus spot in a lobster-eye image is typically composed of a central bright spot and cross arms, and the source point representing the actual location of the target is located in the central bright spot area. The YOLOv5s object detection algorithm model is used to detect and select the cross-focus spot key area containing the source point: first, the input image is preprocessed, the image size is adjusted, and the pixel values ​​are normalized. The YOLOv5s CSPDarknet53 backbone network is used to extract features of the cross-focus spot key area, using FPN (Feature Pyramid Network) and PAN (Path Aggregation Network) to fuse features at different levels. Subsequently, the detection head of the YOLOv5s object detection model predicts the target bounding box and its category score. Finally, non-maximum suppression (NMS) is performed to remove overlapping target bounding boxes, retain the optimal detection result, and obtain the target box information of all cross-focus spot key areas in the image for subsequent processing.

[0057] It should be noted that the YOLOv5s detection model can be replaced with other models in the YOLO series, such as YOLOv5x, YOLOv8s, YOLOv8n, etc., which can all achieve feature extraction. This invention will not go into details.

[0058] S2: The centroid of each point light source in the cross focus key area is used as the position coordinate of each point light source on the target image;

[0059] It should be noted that the central source point of the lobster eye cross focus is generally located at the center of the highlight spot area. Therefore, the present invention intercepts each central highlight spot area based on the target frame information provided by the cross focus key area detection module, and performs the following processing operations on the intercepted image area in sequence: Gaussian filtering for denoising and smoothing, erosion operation to eliminate non-connected areas, specified threshold binarization to screen candidate target source point areas, dilation operation to merge independent bright spots, contour detection to extract contour information of the candidate area, and extraction of the contour centroid as the target source point;

[0060] Specifically, such as Figure 2 As shown, the method for obtaining the position coordinates of each point light source on the target image is:

[0061] S21: Gaussian filtering is performed on the key area of ​​the cross focal spot of each point light source to obtain a filtered image with scattered noise removed and the central bright spot highlighted; preferably, the intercepted image is convolved with a 3×3 pixel two-dimensional Gaussian kernel to remove scattered noise and highlight the central bright spot.

[0062] S22: performing an erosion operation on the filtered image to obtain a connected image with disconnected areas removed;

[0063] S23: performing a binarization operation on the connected image according to a set threshold to obtain a binary image that highlights the candidate areas of each point light source;

[0064] S24: performing a dilation operation on the binary image to obtain a dilated image that merges isolated bright spots;

[0065] S25: Performing contour detection on the expanded image to obtain the contours of each point light source;

[0066] S26: Calculate the centroid of each contour by using the zero-order moment method, and use the centroid of each contour as the position coordinate of the point light source corresponding to each contour on the target image;

[0067] S3: Obtaining the image height of each point light source on the target image according to the position coordinates of each point light source on the target image;

[0068] S4: Obtain the azimuth angle of each point light source relative to the lobster-eye optical detection system based on the image height of each point light source on the target image. The calculation method of the azimuth angle ω' corresponding to any point light source is: Where h is the image height and R is the curvature radius of the lobster-eye optical detection system.

[0069] It should be noted that the conventional focusing imaging principle of the lobster eye optical detection system is as follows Figure 3As shown. Where y represents the image distance of the lobster-eye optical system, x represents the object distance, and R is the radius of curvature of the lobster-eye optical system. The focal length f of the lobster-eye optical system is R / 2, and its object-image relationship is as follows:

[0070]

[0071] When the application scenario is a long-distance, large-field-of-view optical detection with a working distance of 2 km and a working field angle of 42°, it can be assumed that the light source representing the target object enters the system in the form of parallel light. The object distance x of the lobster-eye optical detection system tends to positive infinity, and the image distance y is R / 2. At this time, the parallel light incident condition of the lobster-eye optical detection system can be simplified as follows: Figure 4 As shown. Figure 4 In the equation, y is the image distance and h is the image height. The azimuth angle of the target object relative to the detector and the image height h formed by it will have a geometric relationship as shown in the following equation:

[0072]

[0073] By obtaining the image height information h, the azimuth angle ω′ of the target relative to the detector can be calculated according to the above formula to determine the position of the target object. The image height h formed by the target object can be obtained by processing the image received by the detector. ω′ is the object field angle, and y is the image distance. When the incidence is parallel, the image distance y is R / 2. Substituting R / 2 into the above series of formulas, the azimuth angle calculation formula is as follows:

[0074] As previously mentioned, the coordinates (xi, yi) of each target source point in the detector's received image have been obtained. In this embodiment, the geometric center of the detector's received image corresponds to the center of the lobster-eye lens in the detection system. Therefore, given the pixel size K of the detector's received image and the distance L between the i-th target source point (xi, yi) in the detector's received image and the image's geometric center (x0, y0), the actual image height h of the lobster-eye optical detection system can be calculated as:

[0075]

[0076] h=KL

[0077] The target azimuth angle ω' is obtained based on the image height h and the curvature radius R of the lobster-eye lens. Finally, data integration and visualization are performed to summarize all target position coordinates and azimuth angle information and display them in the form of (xi, yi, ω'). The upper limit of i is the total number of point light sources in the current target image.

[0078] Next, we will build a lobster eye optical detection system in the optical simulation analysis software and perform imaging simulation analysis. Figure 5The simulated image shown contains five targets to be located. In this embodiment, the sample image size is 640×640 pixels, the pixel size of the lobster-eye optical detection system is 2.4 μm, and the curvature radius of the lobster-eye lens is 500 mm. The specific implementation is as follows:

[0079] 1. Cross-focus key area detection based on the YOLOv5s algorithm model

[0080] The cross focus key area detection described in this embodiment is used to detect and filter out the key areas containing the source points in each cross focus in the image before locating the target source point. The YOLOv5s detection algorithm model is used to screen and detect the cross focus key areas to obtain target frame information.

[0081] Based on simulation results from optical simulation analysis software, we constructed a dataset of lobster eye images for training and testing the YOLOv5s detection model. Furthermore, combined with image feature analysis, we used random scaling, random rotation, mosaic data augmentation, mixup data augmentation, and HSV color space transformation data augmentation methods during training to improve model performance. Finally, we used the trained model to detect the sample images and extract the target bounding box information from the detection results for subsequent processing.

[0082] 2. Target source location based on morphological operations

[0083] The central source point of the lobster eye cross focus is generally located at the center of the highlighted spot area. Based on the target frame information provided by the cross focus key area detection module, the key area is intercepted and the source point is located. The specific steps are as follows:

[0084] (1) Gaussian filtering: Preferably, the captured image is convolved with a 3×3 pixel two-dimensional Gaussian kernel to remove scattered noise and highlight the central bright spot.

[0085] (2) Erosion: Preferably, the Gaussian filtered image is eroded with a kernel size of 2 × 2 pixels to further remove stray light and other small non-connected areas.

[0086] (3) Specifying a threshold for binarization: The pixel with the maximum grayscale value of the cross focus will appear at the center of the bright spot. Preferably, the threshold is set to 95% of the maximum grayscale value of the current intercepted image, and a binarization operation is performed to obtain the candidate region of the target source point.

[0087] (4) Dilation: Multiple bright spots may appear in the center of the binarized bright spot. Preferably, dilation is performed on the binarized image to merge them, and the kernel size is set to 2×2 pixels, which is close to the cross arm width.

[0088] (5) Contour detection: After the dilation operation, a binary image is obtained. The openCV toolkit is used to perform contour detection on the binary image to obtain the contour information of the candidate area of ​​the target source point.

[0089] (6) Centroid extraction: After obtaining the outline of the center area of ​​the cross-focus spot, preferably, the centroid is extracted by calculating the zero-order moment of the outline as the central source point of the target cross-focus spot.

[0090] 3. Target Azimuth Calculation of Lobster Eye Optical Detection System

[0091] In this embodiment, the detector pixel size K is 2.4 μm, the lobster-eye lens curvature radius R is 500 mm, and the distance L between the i-th target source point (xi, yi) and the image geometric center point (x0, y0) in the detector received image, the image height h, and the target azimuth angle ω' are calculated as follows:

[0092]

[0093] h=KL

[0094]

[0095] According to the coordinates (xi, yi) of each source point obtained by the target source point positioning module and the above formula, the corresponding azimuth angle ω' is calculated. The integrated position coordinate information and azimuth angle information are stored in the form of (xi, yi, ω') and visualized. In this embodiment, the final positioning effect of the lobster eye optical detection system target positioning method is as follows: Figure 6 As shown by Figure 5 、 Figure 6 It can be seen that the five targets to be located in the sample image are successfully detected and accurately located. They are returned in the sample image in the form of rectangular boxes, solid circles marking the target source points, and additional (xi, yi, ω') labels. At this time, the sequence number of the target to be located is i = 1, 2, 3, 4, 5.

[0096] In summary, the target positioning method for the lobster-eye optical detection system provided by the present invention can be summarized as follows:

[0097] The target image is first detected using the YOLOv5s target detection algorithm model for cross-focus key areas. After image preprocessing to eliminate background light and noise interference, the cross-focus features in the image are extracted using the YOLOv5s CSPDarknet53 Backbone network and the Neck network that combines a feature pyramid network (FPN) and a path aggregation network (PAN). Detection boxes are used to select key areas of each cross-focus spot and obtain corresponding target box information for more accurate target positioning in subsequent steps.

[0098] The key area is intercepted based on the target frame information obtained by the key area detection module, and the center source point of the cross focus representing the actual position of the target is extracted from the intercepted area to obtain the position coordinate information of the target.

[0099] Because the location of the target's cross-focus source can reflect the target's actual azimuth relative to the detector, the present invention performs optical path analysis on the Lobster Eye optical detection system to derive a target azimuth calculation formula. Based on this azimuth calculation formula, the corresponding azimuth is calculated in conjunction with the acquired target source coordinates to obtain the target's relative position information.

[0100] After the above processing, data integration and visualization are carried out to integrate the target position coordinates and orientation information, and finally achieve accurate positioning of the detected target.

[0101] It can be seen that compared with the conventional lobster eye image target detection method, the process design idea described in the present invention starts from the characteristics of lobster eye images, and transforms the target positioning problem of the lobster eye optical detection system into the key point detection problem of the cross focal spot, thereby realizing the acquisition of accurate target position information including position coordinates and their relative azimuth angles, and solving the problems of messy distribution of target crosses in the image and mutual occlusion and interference of cross arms. It has a low false detection rate, good detection and positioning effect, and is more in line with the needs of optical detection.

[0102] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may of course make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A target positioning method applied to a lobster eye optical detection system, characterized in that: The following steps are involved: A deep learning-based target detection model is used to extract the key areas of the cross-focus spot of each point light source in the target image acquired by the lobster-eye optical detection system. The centroid of each point light source in the cross focus key area is used as the position coordinate of each point light source on the target image; Obtaining the image height of each point light source on the target image according to the position coordinates of each point light source on the target image; The azimuth angle of each point light source relative to the lobster-eye optical detection system is obtained based on the image height of each point light source on the target image. The calculation method of the azimuth angle ω' corresponding to any point light source is: Where h is the image height and R is the curvature radius of the lobster-eye optical detection system.

2. The target positioning method applied to the lobster eye optical detection system according to claim 1, characterized in that: The method for obtaining the image height h of any point light source on the target image is: h=KL Among them, K is the pixel size of the detector receiving image in the lobster eye optical detection system, and L is the distance between the position coordinates (xi, yi) of the current point light source on the target image and the geometric center point (x0, y0) of the detector receiving image.

3. The target positioning method applied to the lobster eye optical detection system according to claim 1, characterized in that: The target detection model is a YOLOv5s target detection model, and the YOLOv5s target detection model includes a CSPDarknet53 structure backbone network, a feature pyramid network, a path aggregation network, a detection head, and a non-maximum suppression network; The method for obtaining the key area of ​​the cross focus of any point light source is as follows: The CSPDarknet53 structure backbone network of the YOLOv5s target detection model performs initial feature extraction on the target image; The feature pyramid network and path aggregation network are used in sequence to fuse the extracted initial features at different levels to obtain fused features; The detection head in the YOLOv5s target detection model predicts the target bounding box of the fused features, and obtains the target bounding box corresponding to each point light source and the point light source category score of each target bounding box; The category corresponding to the maximum score of the point light source category of each target bounding box is taken as the point light source category of each target bounding box; The non-maximum suppression network is used to remove the overlapping target bounding boxes belonging to the same point light source category in each target bounding box. For each point light source, only the target bounding box corresponding to the maximum score of the point light source category is retained to obtain the cross focus key area corresponding to each point light source in the target image.

4. The target positioning method applied to the lobster eye optical detection system according to claim 1, characterized in that: The method for obtaining the position coordinates of each point light source on the target image is: Gaussian filtering is performed on the key area of ​​the cross-focus spot of each point light source to obtain a filtered image that removes scattered noise and highlights the central bright spot; Perform an erosion operation on the filtered image to obtain a connected image with disconnected areas removed; Binarize the connected image according to the set threshold to obtain a binary image that highlights the candidate areas of each point light source; Perform a dilation operation on the binary image to obtain a dilated image that merges isolated bright spots; Perform contour detection on the expanded image to obtain the contours of each point light source; The centroid of each contour is calculated by the zero-order moment method, and the centroid of each contour is used as the position coordinate of the point light source corresponding to each contour on the target image.

5. The target positioning method applied to the lobster eye optical detection system according to any one of claims 1, characterized in that: The deep learning-based target detection model is the YOLOv5s target detection model, the YOLOv5x target detection model, the YOLOv8s target detection model, or the YOLOv8n target detection model.

6. A target positioning method for a lobster-eye optical detection system according to any one of claims 1 to 5, characterized in that: The lobster eye optical detection system has a working distance of 2 km and a working field angle of 42°.

7. A target positioning method for a lobster-eye optical detection system according to any one of claims 1 to 5, characterized in that: The following steps are also included: A rectangular frame is used to select and a solid circle is used to mark each point light source on the target image. At the same time, additional data labels (xi, yi, ω') are displayed around the rectangular frame of each point light source on the target image.

Images