Anti-interference automatic identifying and tracking pod system

Abstract

The invention provides an anti-interference automatic identification and tracking pod system, which relates to the technical field of pods and comprises an image acquisition module, a lens temperature control module, a strong light suppression control module, an image identification and tracking module, a camera, a servo control module and a main control module, the temperature adjusting module is used for adjusting the temperature at a camera of the camera so as to reduce the influence of the temperature on the camera; and the strong light suppression control module is used for processing the received image based on a preset strong light suppression algorithm so as to suppress strong light in the image. According to the invention, by adding the lens temperature control module and the strong light suppression control module, the anti-interference capability to the external environment is enhanced, the image definition is enhanced, and the identification and tracking effect of the identification and tracking pod is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of pods, and in particular to an anti-interference automatic identification and tracking pod system. Background Art

[0002] With the rapid development of modern science and technology, intelligent target recognition and tracking pods have been widely used in military, security, traffic monitoring and other fields. However, in complex and dynamic environments, traditional pod systems often face great challenges in target recognition and tracking, especially when encountering external interference (such as ambient temperature interference, strong light interference, etc.). The robustness and accuracy of existing technologies are difficult to meet high-demand application scenarios. Summary of the invention

[0003] The present invention provides an anti-interference automatic identification and tracking pod system to solve the defect that the robustness and accuracy of the prior art are difficult to meet the high-demand application scenarios when encountering external interference (such as ambient temperature interference, strong light interference, etc.).

[0004] The present invention provides an anti-interference automatic identification and tracking pod system, comprising an image acquisition module, a lens temperature control module, a strong light suppression control module, an image recognition and tracking module, a camera, a servo control module and a main control module, wherein the lens temperature control module is arranged at the camera and is used to adjust the temperature at the camera head of the camera to reduce the influence of the temperature on the camera; the image acquisition module is electrically connected to the strong light suppression control module and the camera respectively, and is used to extract images according to the video shot by the camera and send them to the strong light suppression control module; the strong light suppression control module is used to process the received images based on a preset strong light suppression algorithm, and send the processed images to the image recognition and tracking module; the image recognition and tracking module is used to identify a target in the image, track the target in real time, determine the position coordinates of the target and feed them back to the main control module; the main control module is used to output a servo control instruction to the servo control module according to the position coordinates of the target; the servo control module is used to drive the camera to change the angle according to the received servo control instruction to achieve tracking of the target.

[0005] According to an anti-interference automatic identification and tracking pod system provided by the present invention, the camera includes a visible light camera and a thermal imaging camera. Correspondingly, the image acquisition module includes an ISP chip or FPGA chip connected to the visible light camera, an ISP interface connected to the ISP chip or FPGA chip, a USB interface connected to the thermal imaging camera, and an HDMI interface connected to the strong light suppression control module.

[0006] According to an anti-interference automatic identification and tracking pod system provided by the present invention, the strong light suppression control module is used to process the received image based on a preset strong light suppression algorithm, including:

[0007] Calculate the brightness mean and standard deviation of the received image, and compare whether the brightness value of the local area of ​​the image exceeds the global brightness threshold of the image. If so, determine that there is strong light interference in the local area of ​​the image, where the global brightness threshold of the image is determined based on the brightness mean and standard deviation of the image;

[0008] If there is abnormal brightness in a local area of ​​the image, a thermal image captured by the thermal imaging camera is obtained, and data alignment and fusion processing are performed on the image according to the thermal image.

[0009] According to an anti-interference automatic identification and tracking pod system provided by the present invention, the main control module is used to output a servo control instruction to the servo control module according to the position coordinates of the target, including:

[0010] Get the current servo's attitude information;

[0011] According to the position coordinates of the target and the posture information of the current servo, the movement direction and movement speed of the servo are calculated based on the PID control algorithm, and the movement direction and movement speed of the servo are output as servo control instructions to the servo control module;

[0012] Among them, the parameters of the PID control algorithm are negatively correlated with the movement speed of the servo motor, and the PID control algorithm introduces a weighted feedback mechanism of position and speed.

[0013] According to an anti-interference automatic identification and tracking pod system provided by the present invention, the lens temperature control module includes a temperature sensor, a serial port, a temperature control chip and an electric heating film. The temperature sensor is used to obtain the current temperature data of the camera lens and transmit it to the temperature control chip through the serial port. The temperature control chip compares in real time whether the current temperature exceeds a predetermined threshold to determine whether the camera lens needs to be heated.

[0014] According to an anti-interference automatic identification and tracking pod system provided by the present invention, the temperature control chip generates an electric heating film switch signal based on a PWM duty cycle control formula. The PWM duty cycle control formula is calculated according to the current temperature, target temperature, heating PWM ratio and temperature difference, so that the heating speed is proportional to the temperature difference.

[0015] According to an anti-interference automatic identification and tracking pod system provided by the present invention, it is characterized in that the main control module is also used to send the processed image to the display screen for preview, or send it to the network via routing to achieve real-time network preview.

[0016] The anti-interference automatic identification and tracking pod system provided by the present invention enhances the anti-interference capability to the external environment and the image clarity by adding a lens temperature control module and a strong light suppression control module, thereby improving the identification and tracking effect of the identification and tracking pod. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a principle block diagram of the anti-interference automatic identification and tracking pod system provided by the present invention;

[0019] Figure 2 It is a principle block diagram of the image acquisition module provided by the present invention;

[0020] Figure 3 It is a schematic diagram of the processing flow of the strong light suppression control module provided by the present invention;

[0021] Figure 4 It is a schematic diagram of the processing flow of the lens temperature control module provided by the present invention;

[0022] Figure 5 is a principle block diagram of a servo control module provided by the present invention;

[0023] Figure 6 It is a schematic diagram of the processing flow of the image recognition and tracking module provided by the present invention.

[0024] Reference numerals:

[0025] 1-image acquisition module, 2-lens temperature control module, 3-strong light suppression control module, 4-image recognition and tracking module, 5-camera, 6-servo control module, 7-main control module. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In the description of the embodiments of the present application, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the embodiments of the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0028] In the description of the embodiments of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0029] In the embodiments of the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0030] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0031] Faced with the challenges of dynamic and complex scenes and harsh environments, traditional identification and tracking pod systems often have certain limitations, especially in the following aspects:

[0032] Impact of temperature changes: Traditional identification and tracking pod systems cannot adapt to environments with large temperature changes. The lens surface may be fogged, frosted or condensed due to large temperature differences, resulting in blurred images or complete inability to collect valid data. Traditional pod systems usually lack effective defrosting and defogging functions, so the image quality is seriously degraded.

[0033] Influence of strong light: In practical applications, complex lighting changes in the environment may cause significant interference to image recognition and tracking, resulting in reduced robustness of existing systems.

[0034] Combine the following Figure 1-Figure 6 The anti-interference automatic identification and tracking pod system provided by the present invention is described in detail.

[0035] Figure 1 This is a principle block diagram of the anti-interference automatic identification and tracking pod system provided by the present invention, referring to Figure 1 The present invention provides an anti-interference automatic identification and tracking pod system, comprising an image acquisition module 1, a lens temperature control module 2, a strong light suppression control module 3, an image recognition and tracking module 4, a camera 5, a servo control module 6 and a main control module 7, wherein the lens temperature control module 2 is arranged at the camera 5, and is used to adjust the temperature at the camera head of the camera 5 to reduce the influence of the temperature on the camera; the image acquisition module 1 is electrically connected to the strong light suppression control module 3 and the camera 5 respectively, and is used to extract an image according to the video shot by the camera 5 and send it to the strong light suppression control module 3, the strong light suppression control module 3 is used to process the received image based on a preset strong light suppression algorithm, and send the processed image to the image recognition and tracking module 4, the image recognition and tracking module 4 is used to identify a target in the image, track the target in real time, determine the position coordinates of the target and feed them back to the main control module 7, the main control module 7 is used to output a servo control instruction to the servo control module 6 according to the position coordinates of the target, and the servo control module 6 is used to drive the camera 5 to change the angle according to the received servo control instruction to achieve tracking of the target.

[0036] Optionally, the image acquisition module 1 performs real-time acquisition and ISP image processing on the video source data of the camera 5 to provide a data source for the post-processing unit. The lens temperature control module 2 performs precise temperature control on the lens to achieve defogging and defrosting, reduce external interference caused by temperature changes, and thus achieve the purpose of ensuring the clarity of the lens. The strong light suppression control module 3 is used to control and suppress the influence of backlight or strong light on imaging. The recognition and tracking module uses a hardware neural network processing unit to recognize the image, and performs real-time tracking according to the selected target, and feeds back the position coordinates of the tracked target. The servo control module 6 is used to control the rotation of the servo to drive the camera 5 device to change the viewing angle and track the target. The main control module 7 performs processing and scheduling control on the overall system.

[0037] Optionally, the camera 5 device is connected to the video input interface of the image acquisition module 1 through interfaces such as HDMI / USB / MIPI. The MIPI interface of the image acquisition module 1 is connected to the MIPI interface of the strong light suppression control module 3. The serial port interface of the strong light suppression control module 3 is connected to the serial port of the CPU main control module 7, and the output MIPI interface of the strong light suppression control module 3 is connected to the input MIPI interface of the CPU main control. The lens temperature control module 2 is connected to the serial port of the CPU main control module 7 through the TTL serial port mode. The servo control module 6 is connected to the serial port of the main control module 7 through the TTL serial port mode. Use an HDMI transmission line to connect the display screen and the HDMI output interface of the pod system. Use the RJ45 interface of the pod system to connect the computer network port or router.

[0038] The present invention can ensure that the pod system can maintain the clarity of the lens under various temperature changes by adding a lens temperature control module 2, thereby enhancing the anti-interference ability to temperature changes. As for the problem of strong light interference affecting image imaging, the present invention reduces the strong light interference to the pod system during image acquisition by adding a strong light suppression control module 3.

[0039] Optional, such as Figure 2 As shown, the camera 5 includes a visible light camera and a thermal camera, and correspondingly, the image acquisition module 1 includes an ISP chip or FPGA chip connected to the visible light camera, an ISP interface connected to the ISP chip or FPGA chip, a USB interface connected to the thermal camera 5, and an HDMI interface connected to the strong light suppression control module 3. The image acquisition module 1 performs ISP image processing on the raw format video image data input by the camera 5, such as deformity correction, scaling and cropping, color conversion, etc. After the processing is completed, the data is transmitted to the post-processing module through the vpss unit for processing.

[0040] Optional, such as Figure 3As shown, the strong light suppression control module 3 is used to process the received image based on a preset strong light suppression algorithm, including:

[0041] Calculate the brightness mean and standard deviation of the received image, and compare whether the brightness value of the local area of ​​the image exceeds the global brightness threshold of the image. If so, determine that there is strong light interference in the local area of ​​the image, where the global brightness threshold of the image is determined based on the brightness mean and standard deviation of the image;

[0042] If there is abnormal brightness in a local area of ​​the image, a thermal image captured by the thermal imaging camera is obtained, and data alignment and fusion processing are performed on the image according to the thermal image.

[0043] The video image data after ISP processing is transmitted to the strong light suppression control module 3 through the mipi interface, and the strong light suppression control module 3 detects whether the current video image has strong light interference. In the case of strong light interference, the brightness channel (Y) of the video image will change. Therefore, the recognition of strong light interference in the present invention is mainly focused on the brightness abnormality of the Y channel.

[0044] The steps for strong light interference identification are as follows:

[0045] 1. Calculate the mean and standard deviation of brightness:

[0046] By calculating the mean and standard deviation of the Y channel, the overall brightness level of the image can be determined.

[0047] Assuming that the image size is W×H, and each pixel value of the Y channel is Y(x,y), the mean and standard deviation calculation formulas of the Y channel of the image are as follows:

[0048]

[0049] Among them, Mean(Y) is the mean of the Y channel of the image, and StdDev(Y) is the standard deviation of the Y channel of the image.

[0050] By calculating the mean (Y) and the standard deviation (StdDev (Y), it can be determined whether the current image has abnormal brightness.

[0051] 2. Judgment of strong light interference:

[0052] Generally speaking, when there is a strong light interference area in the image, the Y value will increase sharply. For example, the Y value may be close to 255 (maximum brightness), and the standard deviation will also become larger. A threshold value θ can be set. When the brightness value of a certain area in the image exceeds this threshold, it is considered that there is strong light interference in the area. This threshold value θ is usually set based on the brightness mean and standard deviation of the image.

[0053] 3. Detection of local strong light interference area:

[0054] Strong light interference may only appear in certain local areas of the image (for example, the reflection area of ​​the light source). Therefore, the brightness value in the local area can be calculated and checked whether it exceeds the global threshold.

[0055] Assuming that the image is divided into small blocks of size w×h, for each small block B(i,j) the mean of its brightness value can be calculated as follows:

[0056]

[0057] Among them, MeanB(i,j)(Y) is the mean brightness value of the image block B(i,j).

[0058] If the brightness value of a small block is greater than the threshold θ, it is considered that there is strong light interference in the area.

[0059] 4. Threshold-based positioning of bright light areas:

[0060] If the brightness value of the entire image or a local area exceeds a set threshold value θ, these areas can be marked as strong light interference areas.

[0061] If there is no strong light interference, the image data is directly output to the recognition and tracking processing module for processing.

[0062] If there is strong light interference, the strong light suppression control module 3 will simultaneously obtain the image data of the thermal imaging camera 5, and use the thermal image and the visible light image currently affected by the strong light interference to perform data alignment and fusion processing. Since the thermal image is not sensitive to strong light interference, the image after fusion processing will greatly reduce the impact of strong light on the image quality, so that a relatively clear image can be obtained.

[0063] Data alignment and fusion processing are performed on the images according to the thermal image, with the aim of combining the details in the visible light image with the thermal information in the thermal image. Preferably, an appropriate weighting method can be used to reduce the influence of strong light interference while retaining more brightness information in the thermal image.

[0064] Assume that the Y channel data of the visible light image is Y VIS (x, y), the Y channel data of the thermal image is Y IR (x, y), assign different weights to the two channels, such as the visible light weight W VIS and thermal image weight W ir Perform weighted averaging. The weighted averaging formula is as follows:

[0065] Y fused (x,y)=W VIS (x,y)+Y IR (x,y)

[0066] Among them, Y fused (x, y) is the Y channel value after fusion, W VIS and W IR is the weighting coefficient, satisfying W VIS +W IR =1.

[0067] The fusion strategy is as follows:

[0068] Adjust weights based on brightness information

[0069] Calculate the brightness threshold: First, a brightness threshold Τ can be set to identify the bright light area. For example, when Y VIS When (x,y) exceeds a certain threshold, it is considered that there is strong light interference in the area. Adjust weight: When Y VIS When (x, y) is greater than the threshold Τ, reduce the weight W of the visible light image VIS , increase the weight W of the thermal image IR On the contrary, when Y VIS When (x, y) is less than the threshold Τ, increase the weight W of the visible light image VIS , reduce the weight W of the thermal image IR .

[0070] The specific adjustment formula is as follows:

[0071]

[0072] In addition, the weight can be adjusted dynamically based on the local brightness change. In the strong light area, the brightness changes very dramatically, so the brightness standard deviation in the local area can be calculated to determine whether it is a strong light interference area. If the local standard deviation is large, it means that the area may contain strong light interference, and W can be reduced. VIS , increase the weight of thermal image W IR .

[0073] After the fusion is completed, the complete image can be obtained by synthesizing the fused Y channel and other chrominance channels (U and V channels) as follows:

[0074] I fused (x,y)=(Y fused (x,y),U VIS (x,y),V VIS (x,y)

[0075] Finally, the fused image data is output to the recognition and tracking module through the mipi interface for processing.

[0076] like Figure 4As shown, the image recognition and tracking processing module uses the hardware neural network processing unit to perform predictive reasoning calculations on the acquired corrected image, and performs frame identification processing on the identified target. Since the input image has undergone anti-interference data fusion processing, a better recognition effect can be obtained.

[0077] Optional, such as Figure 5 As shown, the main control module 7 is used to output servo control instructions to the servo control module 6 according to the position coordinates of the target, including:

[0078] Get the current servo's attitude information;

[0079] According to the position coordinates of the target and the current posture information of the servo, the movement direction and movement speed of the servo are calculated based on the PID control algorithm, and the movement direction and movement speed of the servo are output as servo control instructions to the servo control module 6;

[0080] Among them, the parameters of the PID control algorithm are negatively correlated with the movement speed of the servo motor, and the PID control algorithm introduces a weighted feedback mechanism of position and speed.

[0081] When a specific target is selected for tracking, the main control module 7 can obtain the real-time target position data fed back by the recognition and tracking processing module in real time, calculate the direction and speed of the servo movement based on the target position and the current servo posture, and gradually move toward the target until the target is located at the center of the image.

[0082] In the pod control system, the PID (proportional-integral-differential) control algorithm is used to achieve precise motion control, and the PID parameters are optimized to improve the responsiveness and stability of the system. The goal of PID algorithm optimization is to adjust the proportional, integral and differential gains (K p ,K i ,K d ) to minimize the system error and improve the system dynamic performance.

[0083] The basic formula of PID controller is as follows:

[0084]

[0085] Where: e(t) = r(t) - y(t) is the error, r(t) is the target position or speed, y(t) is the actual position or speed of the current system, K P is the proportional gain, controlling the current error, K i is the integral gain, which controls the accumulation of historical errors, K d is the differential gain, which controls the rate of change of the error.

[0086] To accommodate the specific needs of a mobile tracking servo, the PID formula can be adjusted based on the dynamic characteristics of the system. For example, if the servo has high speed or large acceleration, the PID parameters may need to be adjusted to reduce overshoot and oscillation.

[0087] For mobile tracking servos, not only the position error e(t) but also the speed error s(t) should be considered. Therefore, a weighted feedback mechanism of position and speed can be introduced to optimize PID control. This can be achieved through the following custom formula:

[0088]

[0089] Where e(t) is the position error, s(t) is the speed error (the difference between the current speed and the target speed), and α is a weight coefficient that adjusts the influence of the position error and speed error on the controller output.

[0090] By monitoring the system response in real time, the PID parameters are gradually adjusted, and performance indicators such as position error, overshoot, and steady-state error are observed until the system can stably track the target trajectory and achieve the expected performance.

[0091] Optional, such as Figure 6 As shown, the lens temperature control module 2 includes a temperature sensor, a serial port, a temperature control chip and an electric heating film. The temperature sensor is used to obtain the current temperature data of the lens of the camera 5 and transmit it to the temperature control chip through the serial port. The temperature control chip compares in real time whether the current temperature exceeds a predetermined threshold to determine whether the lens of the camera 5 needs to be heated.

[0092] The lens temperature control module 2 controls whether to heat the lens by comparing in real time whether the current temperature exceeds a predetermined threshold. The MCU of the lens temperature control module 2 uses the PWM output pin to control the generated signal according to the comparison result, and adjusts the PWM duty cycle to control the switch state. By adjusting the PWM duty cycle, the heating effect of the electric heating film can be accurately controlled to keep the lens at a suitable temperature at all times, avoiding interference from fogging or frosting that affects image imaging.

[0093] Optionally, the temperature control chip generates an electric heating film switch signal based on a PWM duty cycle control formula, and the PWM duty cycle control formula is calculated according to the current temperature, target temperature, heating PWM ratio and temperature difference, so that the heating speed is proportional to the temperature difference.

[0094] The control calculation method is as follows:

[0095] The PWM duty cycle control formula in the lens temperature control module 2 needs to be calculated based on the current temperature, the target temperature (heating threshold, set by the main control module 7), the heating PWM ratio and the temperature difference, so that the heating speed is proportional to the temperature difference. Generally, the larger the temperature difference, the faster the heating speed should be; the smaller the temperature difference, the slower the heating speed should be.

[0096] Target temperature: Heating threshold (target temperature), denoted as T target ;

[0097] Current temperature: The current temperature, denoted as T current ;

[0098] Then the temperature difference: ▽T=T target -T current ;

[0099] Calculate the PWM duty cycle using the square ratio formula:

[0100] D=α·(▽T) 2 +β

[0101] Where: D is the PWM duty cycle (%), α is the proportionality factor that determines the effect of the temperature difference on the PWM, and β is a constant used to avoid the PWM duty cycle being 0 at small temperature differences (for example, to ensure that the heater always has minimum output).

[0102] When the temperature difference is large, the PWM duty cycle increases faster, and when the temperature difference is small, the PWM duty cycle changes more slowly, which helps avoid reducing the heating intensity too quickly when approaching the target temperature.

[0103] Optionally, the main control module 7 is also used to send the processed image to the display screen for preview, or to send it to the network via routing to achieve real-time network preview. The processed image data is distributed and transmitted to the HDMI video output channel and the video encoding channel via the main control module 7. The HDMI channel is responsible for outputting the video image to the display screen, and the video encoding channel is responsible for pushing the video stream data through a network protocol such as RTSP / UDP to achieve real-time network preview.

[0104] In summary, the present invention enhances the anti-interference ability of the external environment in certain scenarios by adding the lens temperature control module 2 and the strong light suppression control module 3, enhances the clarity of the input image, and thus improves the recognition and tracking effect of the recognition and tracking pod. The system can automatically adjust and process images according to complex environments and changing scenes, has stronger anti-interference ability to meet the needs of different environmental tasks, and improves its flexibility. Higher-quality image input greatly improves the recognition effect and tracking ability, and realizes more efficient and stable mobile control.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An anti-interference automatic identification and tracking pod system, characterized in that: The system comprises an image acquisition module, a lens temperature control module, a strong light suppression control module, an image recognition and tracking module, a camera, a servo control module and a main control module. The lens temperature control module is arranged at the camera and is used to adjust the temperature at the camera head of the camera to reduce the influence of the temperature on the camera. The image acquisition module is electrically connected to the strong light suppression control module and the camera respectively and is used to extract images according to the video shot by the camera and send them to the strong light suppression control module. The strong light suppression control module is used to process the received images based on a preset strong light suppression algorithm and send the processed images to the image recognition and tracking module. The image recognition and tracking module is used to identify the target in the image, track the target in real time, determine the position coordinates of the target and feed them back to the main control module. The main control module is used to output a servo control instruction to the servo control module according to the position coordinates of the target. The servo control module is used to drive the camera to change the angle according to the received servo control instruction to achieve tracking of the target.

2. The anti-interference automatic identification and tracking pod system according to claim 1 is characterized in that: The camera includes a visible light camera and a thermal imaging camera. Correspondingly, the image acquisition module includes an ISP chip or FPGA chip connected to the visible light camera, an ISP interface connected to the ISP chip or FPGA chip, a USB interface connected to the thermal imaging camera, and an HDMI interface connected to the strong light suppression control module.

3. The anti-interference automatic identification and tracking pod system according to claim 2 is characterized in that: The strong light suppression control module is used to process the received image based on a preset strong light suppression algorithm, including: Calculate the brightness mean and standard deviation of the received image, and compare whether the brightness value of the local area of ​​the image exceeds the global brightness threshold of the image. If so, determine that there is strong light interference in the local area of ​​the image, where the global brightness threshold of the image is determined based on the brightness mean and standard deviation of the image; If there is abnormal brightness in a local area of ​​the image, a thermal image captured by the thermal imaging camera is obtained, and data alignment and fusion processing are performed on the image according to the thermal image.

4. The anti-interference automatic identification and tracking pod system according to claim 1 is characterized in that: The main control module is used to output a servo control instruction to the servo control module according to the position coordinates of the target, including: Get the current servo's attitude information; According to the position coordinates of the target and the posture information of the current servo, the movement direction and movement speed of the servo are calculated based on the PID control algorithm, and the movement direction and movement speed of the servo are output as servo control instructions to the servo control module; Among them, the parameters of the PID control algorithm are negatively correlated with the movement speed of the servo motor, and the PID control algorithm introduces a weighted feedback mechanism of position and speed.

5. The anti-interference automatic identification and tracking pod system according to claim 1 is characterized in that: The lens temperature control module includes a temperature sensor, a serial port, a temperature control chip and an electric heating film. The temperature sensor is used to obtain the current temperature data of the camera lens and transmit it to the temperature control chip through the serial port. The temperature control chip compares in real time whether the current temperature exceeds a predetermined threshold to determine whether the camera lens needs to be heated.

6. The anti-interference automatic identification and tracking pod system according to claim 5 is characterized in that: The temperature control chip generates an electric heating film switch signal based on a PWM duty cycle control formula, and the PWM duty cycle control formula is calculated according to the current temperature, the target temperature, the heating PWM ratio and the temperature difference, so that the heating speed is proportional to the temperature difference.

7. The anti-interference automatic identification and tracking pod system according to any one of claims 1 to 6, characterized in that: The main control module is also used to send the processed image to a display screen for preview, or to send it to a network via routing to achieve real-time network preview.

Images