Hidden camera detection method, system and device based on cat eye effect and medium

Abstract

The invention discloses a hidden camera detection method and system based on a cat eye effect, computer equipment and a storage medium, and relates to the technical field of laser imaging, and the method comprises the steps: sensing ambient light, carrying out the initialization, and carrying out the parameter adjustment in real time; transmitting a signal through a multispectral light source, and receiving a reflected signal for multiband imaging; carrying out adaptive binarization processing on the image, and calculating a local gray scale probability distribution matrix of the image; and calculating a suspected degree for target detection, and giving an alarm when a camera is recognized. According to the hidden camera detection method based on the cat eye effect, through automatic switching of multi-band light sources, the types of the light sources are optimized, detection light of different wavelengths is emitted, the broad spectrum range is covered, the reflection characteristics of various hidden imaging devices are dealt with, detection is carried out according to imaging of different light sources, and the detection accuracy is improved. The method can be used for detecting the anti-reconnaissance hidden imaging equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of laser imaging, and specifically to a method, system, computer device, and storage medium for detecting hidden cameras based on the cat's eye effect. Background Art

[0002] With the increasing demand for privacy protection, the detection of hidden cameras has become an important issue. Traditional detection methods include radio frequency scanning and infrared imaging, but these methods have limited detection accuracy in complex background environments and are easily affected by external interference. The cat's eye effect is an optical phenomenon. When a camera is irradiated by an external laser, strong backward reflected light will be generated. Using this effect, remote detection and identification of hidden cameras can be achieved.

[0003] Currently, the detection method based on the cat's eye effect mainly adopts the active-passive differential method, which requires an additional trigger circuit to control the emitted laser pulse to synchronize with the frame rate of the detector. However, in practical applications, complex backgrounds and lighting conditions will affect the echo quality of the cat's eye effect, resulting in poor imaging quality, low matching rate, and high false alarm rate of the detection system. Therefore, there is an urgent need for a more robust and efficient method to improve the detection accuracy and response speed of hidden cameras.

[0004] The detection device proposed by the present invention can ensure large-field detection while taking into account the miniaturization of the device and improving portability. By reducing the complexity of the system, enhancing the anti-environmental interference ability, and at the same time reducing the production cost, the device can detect small imaging systems at any time, thus effectively protecting people's privacy and security. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that although traditional hidden camera detection methods can identify some ordinary civilian concealed imaging devices, since they generally use a 650nm light source, the detection effect on specially processed devices is very poor. In addition, it may irradiate brighter objects such as lights, affecting the echo quality of the cat's eye effect, resulting in problems such as poor imaging quality, low matching rate, and high false alarm rate of the detection system, making it difficult to perform efficient and accurate detection. Existing detection methods have certain limitations in terms of the detected camera types, applicable environments, and accuracy rates.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: A hidden camera detection method based on the cat's eye effect, comprising: sensing the ambient light for initialization and performing parameter adjustment in real time; emitting signals through a multi-spectral light source, receiving the reflected signals for multi-band imaging; performing adaptive binarization processing on the image, calculating the local gray probability distribution matrix of the image; calculating the suspicion degree for target detection, and giving an alarm when a camera is recognized.

[0008] As a preferred solution of the hidden camera detection method based on the cat's eye effect according to the present invention, wherein: the initialization includes automatically switching the light source according to the ambient light intensity and the detection parameters set by the user.

[0009] As a preferred solution of the hidden camera detection method based on the cat's eye effect according to the present invention, wherein: the light source switching includes that the time interval of the light source switching meets the usage requirements of slow scanning and automatically cycles.

[0010] As a preferred solution of the hidden camera detection method based on the cat's eye effect according to the present invention, wherein: the emitting signals through a multi-spectral light source includes emitting detection light to the detected area through a multi-band laser, covering the entire range to be detected, and continuously monitoring the environmental changes and adjusting the light source emission parameters in real time.

[0011] As a preferred solution of the hidden camera detection method based on the cat's eye effect according to the present invention, wherein: the receiving the reflected signals for multi-band imaging includes receiving the detection light reflected by the detected area and transmitting the optical signal to a multi-spectral imaging lens and a CMOS for imaging within different spectral ranges of the image to be measured.

[0012] As a preferred solution of the hidden camera detection method based on the cat's eye effect according to the present invention, wherein: the performing adaptive binarization processing on the image includes performing binarization and smoothing filtering preprocessing on the image data, adopting an adaptive threshold algorithm, counting the number of pixels at each gray level, calculating the between-class variance at each threshold from 0 to 255, and taking the maximum value of the between-class variance as the binarization threshold.

[0013] As a preferred solution of the hidden camera detection method based on the cat's eye effect of the present invention, wherein: the parameter adjustment includes that when the processing unit calculates the proportion R of pixels with a gray level of 255 after binarization for each frame of the image after imaging and sends it to the intelligent control module, if the proportion R is greater than 1 / 2, it is considered that the image is too bright, and the control unit reduces the laser emission intensity in steps of 5. When the value has been reduced to 10 and the image is still too bright, the camera gain is reduced. If the image is still feedback as too bright when the gain is reduced to 0x00, a hardware failure is prompted; if the proportion R = 0, it is considered that the image is too dark. At this time, the laser emission intensity is increased in steps of 5. When the value reaches 255 and the image is still prompted as too dark, the camera gain is increased. If the image is still too dark when the gain is increased to 0x10, a hardware failure is prompted.

[0014] As a preferred solution of the hidden camera detection method based on the cat's eye effect of the present invention, wherein: the calculation of the local gray probability distribution matrix of the image includes that in the target detection thread, a two-dimensional coordinate system is constructed with the lower left corner as the coordinate origin, the pixels are traversed, and the point with the highest gray value is recorded as O. When the number of pixels with the highest brightness is greater than 1, the centroid algorithm is used to determine the point O. With the point O as the center and the initial value of the radius r being 2 pixels and the step being 1 pixel, a circle is constructed and the brightness values on the circle are calculated until r is greater than or equal to the radius threshold Lr, which is marked as case 1, or when the brightness values on the circle are all less than the binarization threshold, it stops and is marked as case 2. The radius threshold is related to the parameter settings of the distance D and the brightness L, and is expressed as:

[0015] Lr = (50 / D)*(L / 255)+δ

[0016] Wherein, D is the set test distance, with a range of 1 - 20 meters, L is the set laser brightness, with a range of 0 - 255, and δ is the correction parameter. When the calculated Lr is greater than 50, the value of Lr is taken as 50.

[0017] As a preferred solution of the hidden camera detection method based on the cat's eye effect of the present invention, wherein: the calculation of the suspicion degree for target detection includes that when case 1 occurs, it is considered that the brightness area is too large and there is no target in the detection area; when case 2 occurs, it is considered that the detection area is suspected of having a target, and further confirmation of the shape and roundness is carried out. The gray values of the pixels within the current circle are counted, and the proportion of pixels greater than the binarization threshold in this circle is calculated and recorded as P. The value of P can reflect the shape and roundness of the area and is used as the target suspicion degree.

[0018] As a preferred solution of the hidden camera detection method based on the cat's eye effect of the present invention, wherein: the alarm when a camera is recognized includes that when the processing unit detects a suspected target and calculates the suspicion degree value, the suspicion degree value is input into the alarm module, and the alarm module calculates the alarm signal frequency F according to the suspicion degree, which is expressed as:

[0019] F = P * 100 / 2

[0020] Control the input / output interface IO to generate a square wave signal with a duty cycle of 1 / 2 at frequency F to control the audible and visual alarm; display the acquired image on the screen in real time, and indicate the detection position when there is a concealed imaging device in the detected area.

[0021] Another object of the present invention is to provide a concealed camera detection system based on the cat's eye effect, which can solve the problems of only being able to detect ordinary civilian cameras, being applicable to dark environments, and having low accuracy in the detection of concealed cameras with a 650nm light source by constructing a concealed camera detection system based on the cat's eye effect.

[0022] To solve the above technical problems, the present invention provides the following technical solutions: A concealed camera detection system based on the cat's eye effect, comprising: a light source module for emitting a laser signal to the area to be detected, covering a range of different wavelengths; an imaging module for capturing the laser signal reflected from the target area and generating an image; an intelligent control module for coordinating the work of each module and dynamically adjusting the settings of the light source and the camera; an alarm module for triggering an audible and visual alarm according to the target detection result; and a display module for displaying the image and indicating the position of the suspected camera.

[0023] A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned concealed camera detection method based on the cat's eye effect are implemented.

[0024] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned concealed camera detection method based on the cat's eye effect are implemented.

[0025] The beneficial effects of the present invention: The concealed camera detection method based on the cat's eye effect provided by the present invention automatically switches multi-band light sources, optimizes the types of light sources, emits detection lights of different wavelengths, covers a broad spectrum range, and responds to the reflection characteristics of various concealed imaging devices. Detection is carried out according to the images formed by different light sources, and concealed imaging devices with anti-reconnaissance capabilities can be detected. Adjust the brightness and wavelength of the light source according to different detection environments, reduce the influence of environmental light sources, improve the detection accuracy. The present invention significantly improves the detection efficiency and accuracy, reduces the error caused by environmental light source factors, and enhances the recognition ability of anti-reconnaissance concealment with different reflection characteristics, and can be widely applied to the accurate detection of professional concealed camera identification. Description of the Drawings

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0027] Figure 1 It is the overall flowchart of a hidden camera detection method based on the cat's eye effect provided by an embodiment of the present invention.

[0028] Figure 2 It is a schematic diagram for judging the suspicion degree of a hidden camera detection method based on the cat's eye effect provided by an embodiment of the present invention.

[0029] Figure 3 It is the overall structure diagram of a hidden camera detection system based on the cat's eye effect provided by an embodiment of the present invention.

[0030] Figure 4 It is a schematic diagram of the test environment of a hidden camera detection method based on the cat's eye effect provided by an embodiment of the present invention. Specific Embodiments

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0032] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0033] Embodiment 1

[0034] Referring to Figure 1 - Figure 3 , an embodiment of the present invention provides a hidden camera detection method based on the cat's eye effect, including:

[0035] Step S1: Sense the ambient light for initialization and adjust parameters in real time.

[0036] In step S1, the embodiment of the present invention automatically switches the light source according to the ambient light intensity and the detection parameters set by the user;

[0037] The time interval for light source switching meets the usage requirements of slow scanning and switches automatically in a cycle.

[0038] It should be noted that in an alternative embodiment of the present invention, the system selectively switches different light sources according to the ambient light intensity. For example, when the ambient light is strong, the system automatically switches to the 850nm light source (near-infrared light source) because this light source is more sensitive to the reflection of hidden cameras and is not easily interfered by ambient light. If the ambient light is weak, the 635nm light source (visible light laser) is preferentially selected to improve the detection ability of the light source for small targets. Step S2: Transmit signals through a multi-spectral light source, receive the reflected signals, and perform multi-band imaging.

[0039] Specifically, by real-time sensing of the ambient light conditions, the system can automatically select the most suitable light source, improving the robustness of detection. When there is strong light interference, the 850nm light source has better penetration, avoiding the interference of visible light lasers. In low-light environments, the 635nm light source can provide stronger reflected signals. Therefore, this switching can ensure the efficiency and accuracy of detection. To adapt to the detection requirements in different scenarios and conditions, the automatic adjustment of the light source can ensure that the system always maintains the best detection performance in various environments.

[0040] It should also be noted that for light source switching, it switches automatically in a cycle at intervals of 200 milliseconds. The interval of 200 milliseconds can ensure that the camera acquires 8 frames per second under different light sources, meeting the usage requirements of slow scanning. By adjusting the time interval of light source switching, the system can scan the target area at an appropriate speed and switch between different light sources. This method effectively prevents problems such as signal overload or untimely system response during rapid switching, improving the stability and accuracy of detection.

[0041] Step S2: Transmit signals through a multi-spectral light source, receive the reflected signals, and perform multi-band imaging.

[0042] In step S2, in the embodiment of the present invention, a multi-band laser is used to emit detection light to the detected area, covering the entire area to be detected, while continuously monitoring environmental changes and adjusting the light source emission parameters in real time. When detecting multiple images, image registration is performed;

[0043] It should be noted that by using a multi-band laser, the present invention can simultaneously emit laser signals of multiple wavelengths and adjust the emission parameters in real time according to environmental changes. Lasers of each light source wavelength have different advantages when detecting cameras with different reflection characteristics. Therefore, the system can detect multiple types of cameras using different light sources during a single detection process, providing higher detection efficiency and accuracy.

[0044] Specifically, by integrating lasers of multiple wavelengths in one device, the multi-band laser enables the device to adapt to cameras with various reflection characteristics. By adjusting the wavelength and brightness of the laser in real time, the system can perform efficient detection in different detection environments.

[0045] It should also be noted that in the present invention, the reflected laser signal is received by a multi-band imaging system (for example, infrared and visible light imaging) and the optical signal is transmitted to the CMOS sensor and the multi-spectral imaging lens. This enables the system to image the same target area within different spectral ranges, thereby providing multi-dimensional data for target detection. Lights of different wavelengths have different response characteristics to the signals reflected by different types of hidden cameras. By using multi-band imaging technology, the system can capture a variety of spectral information, thus enhancing the detection ability for various hidden imaging devices (such as ordinary civilian cameras, infrared cameras, and cameras treated with high-transmission films).

[0046] Step S3: Perform adaptive binarization processing on the image and calculate the local gray probability distribution matrix of the image;

[0047] In step S3, the embodiment of the present invention preprocesses the image data by binarization and smoothing filtering, adopts an adaptive threshold algorithm, counts the number of pixels at each gray level, calculates the between-class variance at each threshold from 0 to 255, and takes the maximum value of the between-class variance as the binarization threshold.

[0048] In a feasible embodiment, the image data is preprocessed by binarization and smoothing filtering to improve the contrast between the light spot in the target area and the background. Using the adaptive threshold algorithm, the system selects the optimal binarization threshold by counting the number of pixels at each gray level and calculating the between-class variance. Finally, the system maps the gray values in the image to a binary image to highlight the target area.

[0049] The present invention does not perform processing by adopting a fixed binarization as in the traditional technical solution. Different images may be difficult to directly process due to changes in background light intensity or different reflection characteristics. The adaptive threshold algorithm can dynamically adjust the processing threshold according to the local changes of the signal, improve the image resolution, and reduce overexposure and missed detection.

[0050] Secondly, the between-class variance method can effectively distinguish the target area and the background area of the image, and is particularly suitable for target extraction in complex environments. By maximizing the between-class variance, the system can accurately separate the target and the background in the case of more noise.

[0051] In step S3, in the embodiment of the present invention, after the processing unit receives the imaging, it calculates the proportion R of the pixels with a grayscale of 255 after binarization for each frame of the image and sends it to the intelligent control module. If the proportion R is greater than 1 / 2, it is considered that the image is too bright. The control unit reduces the laser emission intensity in steps of 5. When the value has been reduced to 10 and the image is still too bright, the camera gain is reduced. If the image is still feedback as too bright when the gain is reduced to 0x00, a hardware failure is prompted.

[0052] If the proportion R = 0, it is considered that the image is too dark. At this time, the laser emission intensity is increased in steps of 5. When the value reaches 255 and the image is still prompted as too dark, the camera gain is increased. If the image is still too dark when the gain is increased to 0x10, a hardware failure is prompted.

[0053] Specifically, when the proportion of pixels with a grayscale value of 255 exceeds 50%, the image is considered too bright, and the system will gradually reduce the brightness of the laser or the camera gain; if the image is too dark, the system will increase the laser brightness or gain to ensure that the brightness of the image always meets the requirements.

[0054] Step S4: Calculate the suspicion degree for target detection and alarm when a camera is recognized.

[0055] In step S4, in the embodiment of the present invention, in the target detection thread, a two-dimensional coordinate system is constructed with the lower left corner as the coordinate origin, and the pixels are traversed to obtain the point with the highest grayscale value and denoted as O. When the number of pixels with the highest brightness is greater than 1, the centroid algorithm is used to determine point O. With point O as the center and the initial value of the radius r being 2 pixels, stepping 1 pixel, a circle is constructed and the brightness values on the circle are calculated until r is greater than or equal to the radius threshold Lr, marked as case 1, or when the brightness values on the circle are all less than the binarization threshold, stop and mark as case 2, where the radius threshold is related to the parameter settings of the distance D and the brightness L, expressed as:

[0056] Lr = (50 / D)*(L / 255)+δ

[0057] Among them, D is the set test distance, with a range of 1 - 20 meters, L is the set laser brightness, with a range of 0 - 255, and δ is the correction parameter. When the calculated Lr is greater than 50, Lr takes the value of 50.

[0058] The centroid algorithm can accurately determine the center position of the target and is especially suitable for capturing targets with obvious light spots (such as the echo reflected by the camera). This enables the system to efficiently locate the target and reduce errors. By gradually expanding the circular area and calculating the brightness values, the system can accurately judge the range of the target light spot and further improve the accuracy of target detection.

[0059] When case 1 occurs, it is considered that the brightness area is too large and there is no target in the detection area;

[0060] When situation 2 occurs, the detection area is considered a suspected target, and further confirmation of the shape and roundness is carried out. The gray values of the pixels within the current circle are counted, and the proportion of the pixels greater than the binarization threshold in this circle is calculated and denoted as P. The value of P can reflect the shape and roundness of the area and is used as the target suspicion degree. By calculating the shape and roundness of the light spot area, the target area and background noise can be effectively distinguished, reducing false alarms. This method also has high precision for the detection of small targets.

[0061] Refer to Figure 2 Taking the common elliptical interference reflection as an example, the calculation of the suspicion degree is shown. Here, r is the radius of the circle where the gray values are all less than the binarization threshold. The white area is the actual light spot, and the gray area is the circle with radius r. The suspicion degree is the proportion of the white light spot area on the circle. It can be seen that the rightmost image is the identification of the existence of a hidden camera.

[0062] In step S4, when the processing unit in the embodiment of the present invention detects a suspected target and calculates the suspicion degree value, the suspicion degree value is input into the alarm module. The alarm module calculates the alarm signal frequency F according to the suspicion degree, which is expressed as:

[0063] F = P * 100 / 2

[0064] Control the input / output interface IO to generate a square wave signal with a 1 / 2 duty cycle at frequency F to control the audible and visual alarm;

[0065] The acquired image is displayed on the screen in real time. When there is a hidden imaging device in the detected area, the detection position is indicated.

[0066] Through the confirmation of the shape and roundness and the calculation of the suspicion degree, the system can efficiently identify and locate the reflection signal of the hidden camera. By combining shape analysis and suspicion degree evaluation, the target recognition ability in a complex background can be improved, quickly attracting the attention of the operator.

[0067] Embodiment 2

[0068] Refer to Figure 3 As an embodiment of the present invention, a hidden camera detection system based on the cat's eye effect is provided, including: a light source module 100, an imaging module 200, an intelligent control module 300, an alarm module 400, and an imaging module 500.

[0069] Among them, the light source module 100 is used to emit multi-band laser signals to the detection area, covering the possible reflection characteristics of different types of hidden cameras. Specifically, the light source module 100 can emit multi-band laser signals, including visible light and infrared light, to adapt to different types of optical imaging devices. According to the feedback of the lighting conditions in the detection environment and the target position, the wavelength and brightness of the laser are dynamically adjusted. When the ambient light changes greatly, the system can ensure the quality of the reflected signal by adjusting the laser parameters in real time, making the target features clearly visible.

[0070] Specific implementation method: The light source module 100 dynamically controls the brightness and wavelength of the laser through the parameter input provided by the intelligent control module 300. For example, in a complex environment (such as when there is strong background light interference), the light source module 100 will increase the laser brightness to enhance the reflected signal; when the target area is darker, the laser wavelength will switch to the infrared band to ensure the detection effect.

[0071] The imaging module 200 is used to receive the reflected signals of the objects in the detection area and generate multi-band images. Specifically, it uses multi-spectral imaging technology to capture the reflected signals of the target area and generate images covering multiple bands (infrared, visible light). During the optical signal acquisition, through the built-in image processing unit, the image is preliminarily processed, including operations such as noise reduction and enhancement, to ensure that the image is suitable for subsequent analysis steps.

[0072] Specific implementation method: The imaging module 200 captures the optical signals of the target area through a high-precision sensor (such as CMOS), and with the support of a real-time processing chip, enhances the target features while eliminating noise interference. When the light conditions change, the module can automatically adjust the exposure time and gain parameters according to the instructions of the intelligent control module 300 to adapt to different environments.

[0073] The intelligent control module 300, as the core module of the system, is responsible for coordinating the work of each module and optimizing the operating state of the system in real time according to the feedback. The processing unit in the intelligent control module contains a high-performance processor and a storage unit. Specifically, it includes adjusting the wavelength and brightness of the light source module 100 in real time to optimize the laser emission parameters. According to the image quality feedback from the imaging module 200, dynamically adjusting the gain and exposure time of the camera to ensure the clarity and usability of the image. Integrating the analysis results of the processing unit, optimizing the overall detection strategy, and improving the system robustness. Preprocessing the input image data, including operations such as denoising, enhancement, and adaptive binarization. Extracting target features from the image, such as brightness, shape, roundness, etc., screening and identifying the target area. Calculating the target suspicion degree according to the extracted features to determine whether there is a hidden camera.

[0074] Specific implementation method: The intelligent control module 300 forms a closed-loop control logic by continuously obtaining the feedback from the processing unit. For example, when the processing unit identifies that the image background is too bright, the intelligent control module 300 will reduce the light source brightness or camera gain; when the reflected signal in the target area is too weak, it will increase the laser power and appropriately extend the exposure time. The adaptive binarization algorithm is adopted to dynamically adjust the image processing threshold, minimizing the interference of background noise on the detection result. For example, the processing unit will count the distribution of image gray values and determine the optimal parameters for binarization based on the between-class variance. At the same time, the processing unit identifies possible target reflection areas through shape analysis and brightness comparison.

[0075] The alarm module 400 is used to emit an audible and visual alarm signal according to the detection result of the processing unit to prompt the user's attention. Specifically, when a suspected hidden camera is detected, an audible and visual alarm is triggered. The frequency and intensity of the alarm are dynamically adjusted according to the suspected degree of the target. Provide real-time alarm feedback to the user, and help the user respond quickly by adjusting the intensity of the alarm signal.

[0076] Specific implementation method: The alarm module 400 generates an alarm signal according to the feedback data of the processing unit and controls the audible and visual alarm device through a square wave signal. For example, when the suspected degree of the target is high, the alarm module 400 will increase the frequency of the audible and visual signal to increase the urgency of the alarm.

[0077] The display module 500 is used to display the detection image in real time and mark the position of the hidden camera. Specifically, it includes displaying the image generated by the imaging module 200 in real time and marking the target position after detecting a suspected target. Provide a user operation interface to display alarm information, target features, and relevant data.

[0078] Specific implementation method: The display module 500 presents the results of the processing unit in the form of images and text. For example, when a hidden camera is detected, the display module 500 will mark the target area on the image in real time and provide the suspected degree data to help the user judge the possibility of the target.

[0079] The light source module 100 cooperates with the imaging module 200: The light source module 100 emits a laser signal, and the imaging module 200 receives the reflected light and generates image data, providing a basis for subsequent processing.

[0080] The imaging module 200 cooperates with the processing unit: The image data of the imaging module 200 is received and analyzed by the processing unit, and the processing unit preprocesses the data, extracts features, and detects targets.

[0081] The processing unit cooperates with the intelligent control module 300: The processing unit analyzes the features of the target area, feeds back data to the intelligent control module 300, and the intelligent control module 300 adjusts the light source and camera parameters according to the analysis result.

[0082] The processing unit collaborates with the alarm module 400: The processing unit transmits the detection result of the suspected target to the alarm module 400, and the alarm module 400 issues an alarm signal according to the suspicion degree.

[0083] The alarm module 400 collaborates with the display module 500: After the alarm module 400 is triggered, the display module 500 synchronously marks the target position and provides relevant detection data.

[0084] Specifically, a multi-band laser is used to emit laser signals to the target area. These signals cover the visible light and infrared light bands to adapt to the reflection characteristics of different types of hidden cameras. The emission parameters of the laser signals (including wavelength and brightness) can be adjusted in real time according to the detection environment. For example, in the presence of strong background light interference, it automatically switches to the infrared band and increases the brightness to enhance the reflected signal.

[0085] Specifically, a multi-spectral imaging device is used to receive the reflected light signals from the target area and generate multi-band image data. This device is equipped with a highly sensitive optical sensor that can capture the reflection information at multiple angles and different wavelengths. At the same time, the built-in image processing chip preprocesses the data, including noise reduction, brightness enhancement, and dynamic range adjustment, to ensure that the image quality is suitable for subsequent analysis.

[0086] Specifically, after the image data is input into the high-performance processing unit, preprocessing operations are first performed. The processing unit uses an adaptive binarization algorithm to highlight the bright spot areas in the image that may contain target features. By calculating the gray probability distribution of each pixel and combining the between-class variance to determine the optimal threshold, this algorithm can dynamically adapt to complex backgrounds and exclude redundant noise.

[0087] Specifically, the processing unit further extracts features and filters regions from the preprocessed image data. Based on parameters such as the brightness, shape, size, and roundness of the target area, possible reflection targets are identified. The typical cat's-eye effect signal of a hidden camera usually appears as a high-brightness and nearly circular light spot. By matching with the feature patterns established from experimental data, the processing unit can score the suspicion degree of the target.

[0088] Specifically, when the suspicion degree score of a certain area exceeds the set threshold, the system will determine it as a hidden camera. Subsequently, the detection result is presented in real time in the form of an image through the display device, and the position of the target is marked. At the same time, the audible and visual alarm device is triggered. The frequency and intensity of the alarm device are determined by the suspicion degree of the target area. The higher the suspicion degree, the more urgent the alarm, reminding the user to pay attention.

[0089] Based on the deficiencies of traditional methods being easily interfered by the environment and having a high false alarm rate, the present invention combines a multi-band laser, a multi-spectral imaging device, and adaptive feature analysis technology to significantly improve the accuracy and robustness of concealed camera detection in complex environments.

[0090] Specifically, in the application scenario, when the target area is in a dim light or complex reflection environment, the system can automatically adjust the wavelength and brightness of the laser in real-time monitoring, while optimizing the exposure time and gain parameters of the imaging device. For example, in low light conditions, the system will switch to the infrared laser band and extend the exposure time to ensure that the reflected signal is strong enough to generate a clear image.

[0091] Specifically, in the wide-area detection scenario, the system can achieve coverage detection of large-range targets through high-sensitivity multi-spectral imaging technology. For example, in a complex indoor environment, the multi-band imaging device can capture the specific echo signal of the concealed camera, and through the real-time analysis of the high-performance processing unit, quickly identify the specific location of the hidden camera.

[0092] Specifically, when the detection task involves a target with higher concealment (such as a pinhole camera), the processing unit will combine machine learning algorithms to deeply analyze the target features. By comparing the bright spot features with the known cat-eye effect reflection patterns, the system can significantly improve the recognition accuracy in complex environments and reduce missed alarms and false alarms.

[0093] Through the above specific implementation manners, the present invention exhibits excellent performance in concealed camera detection. Especially in an environment with variable light conditions and complex reflection characteristics, it can efficiently and accurately complete target detection, providing a fast and reliable detection solution for users.

[0094] Example 3

[0095] An embodiment of the present invention, which is different from the previous two embodiments, is as follows:

[0096] Specifically, if the method for detecting a concealed camera based on the cat-eye effect is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0097] The computer program includes a number of instructions that enable a computer device (such as a server, a personal computer, or other network devices) to perform the following steps:

[0098] Control a multi-band laser to emit laser signals towards a target area, including visible light and infrared light bands, covering different characteristic reflection points of the target area. The laser dynamically adjusts the emission parameters (such as wavelength, intensity, and pulse frequency) according to environmental conditions.

[0099] Use a multi-spectral imaging device to receive the reflected light signals of the target area and generate multi-band image data. The device captures multi-angle images through high-sensitivity sensors (such as CMOS or CCD), and at the same time adjusts the exposure time and gain parameters to adapt to different lighting conditions.

[0100] Preprocess the collected image data, including operations such as denoising, dynamic range compression, and gray level equalization, to improve the usability of the data. An adaptive binary algorithm is adopted to determine the optimal threshold according to the gray level probability distribution of the image.

[0101] Extract the key features of the target reflection area from the preprocessed image, including parameters such as brightness, shape, size, and roundness. Use edge detection algorithms to extract the region contour, and at the same time combine morphological analysis to further improve the feature extraction results. For complex targets, deep feature analysis is performed through machine learning algorithms.

[0102] Input the extracted target features into a target detection model, and by matching with the predefined echo feature pattern of the cat-eye effect, determine whether there are hidden camera reflection points in the image. If a target exists, mark the target position according to the coordinate information output by the model, and evaluate the suspicion degree of the target area.

[0103] Based on the target detection result, when the score of the suspected target exceeds the set threshold, the system triggers an audible and visual alarm device to provide real-time feedback. At the same time, mark the target area on the image through a display device, and the user can view the target position and its related attribute information.

[0104] The system classifies the target according to the detection result, for example, determines the type of the hidden camera (pinhole camera, ordinary camera, etc.). Generate a detection report containing the target type, position, features, and suspicion degree score, and export the report for subsequent analysis or archiving.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definite ordered listing of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.

[0106] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0107] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0108] The computer program of the present invention further supports real-time operations. For example, through a communication interface with a drone, real-time image acquisition and preprocessing are achieved, and the detection results are directly pushed to the control terminal after defect detection. Compared with traditional detection methods, this program can significantly reduce manual intervention and provide more efficient and accurate exterior wall defect detection services.

[0109] Example 4

[0110] Refer to Figure 4, which is an embodiment of the present invention, provides a method for detecting hidden cameras based on the cat's eye effect. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through simulation experiments.

[0111] The experiments were carried out in two scenarios to simulate different complex environments.

[0112] Indoor environment: A standard hotel room with an area of 20 square meters and a height of 3 meters was selected. The room contains common furniture (such as beds, tables, chairs, and wardrobes), as well as glass windows and mirrors to increase reflection interference.

[0113] Public place: A conference room with an area of 50 square meters and a height of 4 meters was selected. It has multiple groups of tables and chairs and a large glass curtain wall, and there is some metal decoration on the walls.

[0114] Target device: A pinhole camera with a lens diameter of 1 mm and the lens coated with an 850 nm high-transmission film (anti-reconnaissance camera). Such cameras are commonly used for covert monitoring and have strong anti-interference capabilities, especially suitable for testing the effectiveness of the cat's eye effect detection technology. Light sources used:

[0115] 635 nm light source: Visible light laser, suitable for testing the detection ability for objects with strong reflection.

[0116] 850 nm light source: Near-infrared laser, with strong penetration ability, suitable for testing the detection ability for covert cameras.

[0117] The test range is from 0.5 meters to 15 meters, and based on this range, the detection ability of the detection system at different distances is verified.

[0118] Ambient light conditions: The tests were carried out under natural indoor light to simulate the detection conditions in ordinary homes and office environments.

[0119] The test environment was set as a standard indoor space. Assuming the room width is 10 meters, the test points were arranged at different distances (0.5 m, 1 m, 2 m, 5 m, 10 m, 15 m).

[0120] The pinhole cameras were placed at different heights and angles to simulate different covert positions. The test points at different distances will help evaluate the performance of the detection system in terms of distance.

[0121] The placement method of the camera is as Figure 4As shown. After the device is powered on, the processing unit sends the initial parameters to the intelligent control module 300, and the intelligent control module 300 adjusts the laser emission intensity and light source selection according to the parameters. After the laser emits a beam, it is projected onto the area to be measured through the optical path system. When the laser irradiates the area to be measured, the reflected and scattered light returns to the device, enters the lens module after being split by the beam splitter, and forms an image. The processing unit continuously reads the image data from the lens module, performs image analysis, and judges the image quality and the detection of suspicion degree. If a suspicious object is detected, a target mark is made on the image, the image is sent to the display module 500 and presented on the screen, and an alarm is made according to the suspicion degree. If no suspicious object is detected, the expected parameters are recalculated according to the image quality, and the calculated parameters are sent to the intelligent control module 300 to adjust the laser. Thus, the measurement link forms a closed loop.

[0122] Manual test:

[0123] Step 1: Switch to the 850nm light source.

[0124] First, irradiate the camera lens at a distance of 20 meters and observe the imaging image. At this time, there are no bright spots on the image, indicating that the reflected signal cannot be detected at this distance.

[0125] Gradually shorten the distance, and shorten it to each test point such as 15 meters, 10 meters, 5 meters, 3 meters, 2 meters, 1 meter, etc. each time. Until 0.8 meters, a faint bright spot will appear on the image. When the distance is shortened to 0.5 meters, the light spot in the image shows a high-brightness state, indicating that the detection system can accurately identify the target and display a strong echo signal.

[0126] Record the image and the detected bright spot data at this time to verify the detection ability of the detection system at different distances.

[0127] Step 2: Switch to the 635nm light source.

[0128] Under the same test environment, manually switch to the 635nm light source. Conduct a similar step-by-step distance test again.

[0129] When irradiating the lens at 20 meters, there are still no bright spots on the image. Continue to shorten the distance to 10 meters, and a high-brightness light spot begins to appear in the image. Further shorten the distance, and the detection system will continuously display a high-brightness light spot, proving that the detection range under this light source is smaller than that of the 850nm light source.

[0130] Automatic test:

[0131] Step 1: Automatically switch the light source mode.

[0132] Switch to the automatic mode, and the system will automatically alternately switch between the 635nm and 850nm light sources according to the actual detection situation.

[0133] When irradiating the camera at a distance of 20 meters, there are no bright spots on the image. Gradually shorten the distance to 10 meters. At this time, high-brightness light spots begin to appear in the image, indicating that the system can detect the echo signal of the hidden camera.

[0134] Step 2: Detection accuracy and response speed.

[0135] Within the distance range of 0.5 meters to 15 meters, test the response speed and accuracy of the system. Ensure that the system can quickly switch the light source and accurately detect the camera reflection signal.

[0136] During the test, record the brightness, size, and signal intensity of the light spots, and evaluate the detection accuracy and response speed of the system at different distances. The experimental data should include the following aspects:

[0137] Detection distance and time of appearance of bright spots. Record the minimum distance at which the detection system can successfully detect the light spot at different distances (from 20 meters to 0.5 meters) under each light source.

[0138] Record the response time of each detection (i.e., the time from the appearance to the determination of the bright spot in the image).

[0139] Image brightness and size of light spots. During each test, record the brightness value of the image bright spot (e.g., the relative value of brightness intensity), as well as the diameter or area of the light spot.

[0140] Compare the changes in the reflection intensity of the image under different light sources (635nm and 850nm).

[0141] Detection error and missed detection. Statistically analyze whether there are false detections (false alarms of bright spots) or missed detections (failure to detect the target) under different distances and different light source conditions of the system. Analyze the number of false detections and missed detections and the reasons. The experimental results are shown in Table 1.

[0142] Table 1 Comparison table of experimental results

[0143]

[0144]

[0145] In the manual mode under the 850nm light source, no obvious bright spots can be detected at a relatively long distance (>10 meters), while when approaching (<2 meters), the bright spots gradually increase, and the brightness of the light spot reaches the highest value at 0.5 meters, which is suitable for detecting long-distance hidden devices.

[0146] Under the 635nm light source, the detection effect at a relatively long distance is better than that of 850nm, but the detection range is smaller, and a stronger light spot can be displayed within 10 meters, which is suitable for detecting at a relatively short distance.

[0147] In the automatic mode, the present invention successfully captures light spots at different distances by switching the light source, demonstrating strong adaptability. It automatically adjusts the light source to adapt to changes in ambient light conditions, showing higher detection efficiency and stability.

[0148] No missed detections or false detections occurred in this experiment, indicating that the cat-eye effect detection method of the present invention exhibits very high precision in the experimental scenario. The hidden camera detection method based on the cat-eye effect of the present invention significantly improves the detection efficiency and accuracy through a multi-band laser, multi-spectral imaging, and automated processing. Compared with traditional methods, the experimental results show that the method of the present invention has the following advantages: shorter detection time, higher accuracy and recall rate, and stronger environmental adaptability.

[0149] The experiment verifies the superiority of the present invention in complex environments and against anti-reconnaissance cameras, and can meet the rapid detection requirements of hidden cameras in multiple scenarios. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A hidden camera detection method based on cat's eye effect, characterized in that: include: Sense ambient light to initialize and adjust parameters in real time; The signal is transmitted through a multi-spectral light source, and the reflected signal is received to perform multi-band imaging; Perform adaptive binarization on the image and calculate the local grayscale probability distribution matrix of the image; Calculate the suspicion degree to detect the target and issue an alarm when the camera is recognized.

2. The method for detecting hidden cameras based on the cat's eye effect as claimed in claim 1, characterized in that: The initialization includes, The light source is automatically switched according to the ambient light intensity and the detection parameters set by the user.

3. The hidden camera detection method based on the cat's eye effect as claimed in claim 1 or 2, characterized in that: The light source switching includes: The light source switching time interval meets the requirements of slow scanning and automatically switches in a cycle.

4. The hidden camera detection method based on the cat's eye effect as claimed in claim 1 or 2, characterized in that: The transmitting of the signal by the multi-spectral light source comprises: The detection light is emitted to the detected area through a multi-band laser, covering the entire range to be detected. At the same time, the environmental changes are continuously monitored and the light source emission parameters are adjusted in real time.

5. The hidden camera detection method based on the cat's eye effect as claimed in claim 4, characterized in that: The receiving reflected signal to perform multi-band imaging includes: Receive the detection light reflected by the detected area and transmit the optical signal to the multi-spectral imaging lens and CMOS to image the image to be tested in different spectral ranges.

6. The method for detecting hidden cameras based on the cat's eye effect as claimed in claim 5, characterized in that: The adaptive binarization processing of the image comprises: The image data is preprocessed by binarization and smoothing filtering. The adaptive threshold algorithm is used to count the number of pixels at each gray level, calculate the inter-class variance at each threshold from 0 to 255, and take the maximum inter-class variance as the binarization threshold.

7. The method for detecting hidden cameras based on the cat's eye effect as claimed in claim 6, characterized in that: The parameter adjustment includes: After receiving the image, the processing unit calculates the pixel ratio R of each frame with a grayscale of 255 after binarization and sends it to the intelligent control module. If the ratio R is greater than 1 / 2, the image is considered too bright. The control unit reduces the laser emission intensity in steps of 5. When the value has been reduced to 10 and the image is still too bright, the camera gain is reduced. If the image is still too bright when the gain is reduced to 0x00, it indicates a hardware fault. If the ratio R=0, the image is considered too dark. At this time, increase the laser emission intensity in steps of 5. When the value reaches 255 and the image is still too dark, increase the camera gain. If the image is still too dark when the gain is increased to 0x10, it indicates a hardware failure.

8. The method for detecting hidden cameras based on the cat's eye effect as claimed in claim 7, characterized in that: The local grayscale probability distribution matrix of the calculated image, include, In the target detection thread, a two-dimensional coordinate system is constructed with the lower left corner as the origin of the coordinate system, and the pixels are traversed to obtain the point with the highest grayscale value, which is recorded as O. When the number of pixels with the highest brightness is greater than 1, the centroid algorithm is used to determine point O. With point O as the center of the circle, the radius r is initially set to 2 pixels, and the step is 1 pixel. A circle is constructed and the brightness value on the circle is calculated until r is greater than or equal to the radius threshold Lr, marked as case 1, or the brightness values ​​on the circle are all less than the binarization threshold, marked as case 2, where the radius threshold is related to the parameter settings of distance D and brightness L, expressed as: Lr=(50 / D)*(L / 255)+δ Wherein, D is the set test distance, ranging from 1 to 20 meters, L is the set laser brightness, ranging from 0 to 255, and δ is the correction parameter. When the calculated Lr is greater than 50, Lr takes the value of 50.

9. The method for detecting hidden cameras based on the cat's eye effect as claimed in claim 8, characterized in that: The calculation of the suspicion degree for target detection includes: When situation 1 occurs, it is considered that the brightness area is too large and there is no target in the detection area; When situation 2 occurs, the detection area is considered to be a suspected target, and the shape and roundness are further confirmed. The grayscale values ​​of the pixels in the current circle are counted, and the proportion of pixels greater than the binary threshold in this circle is calculated and recorded as P. The P value can reflect the shape and roundness of the area and serve as the target suspicion.

10. The hidden camera detection method based on cat's eye effect as claimed in claim 9, characterized in that: The alarm is issued when the camera is identified, including: When the processing unit detects a suspected target and calculates the suspicion value, the suspicion value is input into the alarm module, and the alarm module calculates the alarm signal frequency F according to the suspicion value, which is expressed as: F=P*100 / 2 The control input / output interface IO generates a square wave signal with a 1 / 2 duty cycle at a frequency F to control the sound and light alarm; The acquired image is displayed on the screen in real time, and when there is a hidden imaging device in the detected area, the detection location is indicated.

11. A hidden camera detection system based on cat's eye effect, characterized in that: include: A light source module (100) is used to emit a laser signal to the area to be detected, covering a range of different wavelengths; An imaging module (200) is used to capture the laser signal reflected from the target area and generate an image; An intelligent control module (300) is used to coordinate the work of various modules and dynamically adjust the settings of the light source and the camera; An alarm module (400) is used to trigger an audible and visual alarm according to the target detection result; The display module (500) is used to display images and indicate the location of the suspected camera.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the hidden camera detection method based on the cat's eye effect are implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hidden camera detection method based on the cat's eye effect are implemented.

Images