Method and apparatus for determining state of safety protection device, and readable storage medium

Abstract

A method and apparatus for determining the state of a safety protection device, and a readable storage medium. The method comprises: acquiring a scene image and a target feature of a safety protection device, wherein the scene image is used for indicating the usage scene of the safety protection device, and the target feature is used for indicating an appearance feature and / or a state feature of the safety protection device; on the basis of the target feature, identifying a target region from the scene image, wherein the target region includes the region where the safety protection device is located; and on the basis of the target region, determining a target state of the safety protection device, wherein the target state is used for indicating that the safety protection device is in a safely worn state or an unworn state. The present invention solves the technical problem of it being impossible to determine the use state of a safety protection device.

Description

Methods, apparatus and readable storage media for determining the state of safety equipment

[0001] This application claims priority to Chinese Patent Application No. 202411585228.1, filed on November 7, 2024, entitled "Method, Apparatus and Readable Storage Medium for Determining the State of a Safety Device", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of security monitoring technology, and more specifically, to a method, apparatus, and readable storage medium for determining the status of a security device. Background Technology

[0003] Currently, safety belts are crucial equipment for protecting the lives of workers engaged in high-altitude operations. They serve as the last line of defense for worker safety. Monitoring and determining the status of safety equipment is a critical step in ensuring worker safety.

[0004] In related technologies, due to various reasons, workers may unexpectedly lose the protection of safety equipment during operations, posing a significant safety risk. Currently, there is a lack of a method to promptly and accurately determine whether workers at height have lost the protection of their safety equipment. Therefore, there is a technical problem of being unable to determine the operational status of safety equipment.

[0005] There is currently no effective solution to the aforementioned technical problem of being unable to determine the usage status of safety equipment. Summary of the Invention

[0006] This invention provides a method, apparatus, and readable storage medium for determining the state of a security device, thereby at least addressing the technical problem of being unable to determine the usage state of a security device.

[0007] According to one aspect of the present invention, a method for determining the state of a security device is provided. The method may include: acquiring a scene image and target features of the security device, wherein the scene image indicates the usage scenario in which the security device is located, and the target features indicate the appearance and / or state features of the security device; identifying a target region from the scene image based on the target features, wherein the target region includes the region where the security device is located; and determining a target state of the security device based on the target region, wherein the target state indicates whether the security device is in a safe wearing state or an unworn state.

[0008] Optionally, acquiring scene images of the security device includes: acquiring environmental information of the security device, wherein the environmental information is used to indicate the weather and / or lighting environment in which the security device is located; adjusting the acquisition angle of the acquisition device based on the environmental information; and controlling the acquisition device to acquire scene images of the security device according to the acquisition angle.

[0009] Optionally, based on target features, the target region is identified from the scene image, including: segmenting the scene image to obtain multiple sub-scene images; extracting scene features from each sub-scene image; and determining the region of the sub-scene image corresponding to the scene features as the target region in response to matching the scene features with the target features.

[0010] Optionally, the target state of the security device is determined based on the target features, including: inputting the target features into the recognition model for analysis to obtain the target state of the security device.

[0011] Optionally, the target features are input into the recognition model for analysis to obtain the target state of the safety device, including: comparing the target features with preset features to obtain a comparison result, wherein the preset features are used to indicate the appearance features and / or state features of the safety device in a safe wearing state; in response to the comparison result that the target features match the preset features, the target state is determined to be a safe wearing state.

[0012] Optionally, the method for determining the status of a security device may further include: preprocessing the scene image.

[0013] According to another aspect of the present invention, a device for determining the state of a security device is also provided. The device may include: an acquisition unit, configured to acquire a scene image and target features of the security device, wherein the scene image indicates the usage scenario of the security device, and the target features indicate the appearance and / or state features of the security device; an identification unit, configured to identify a target area from the scene image based on the target features, wherein the target area includes the area where the security device is located; and a determination unit, configured to determine a target state of the security device based on the target area, wherein the target state indicates whether the security device is in a safe wearing state or not worn state.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the storage medium is located to execute the method for determining the state of the security device according to the present invention.

[0015] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program executes a method for determining the state of a security device according to embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided. The program product includes computer instructions that, when executed by a processor, implement the method for determining the state of a security device according to the embodiments of the present invention.

[0017] In this embodiment of the invention, a scene image and target features of a security device are acquired. The scene image indicates the usage scenario of the security device, and the target features indicate the appearance and / or status characteristics of the security device. Based on the target features, a target region is identified from the scene image, wherein the target region includes the area where the security device is located. Based on the target region, the target state of the security device is determined, wherein the target state indicates whether the security device is in a safe wearing state or not wearing a safe wearing state. In other words, this invention analyzes the acquired scene image and target features of the security device to determine its target state, achieving the purpose of real-time monitoring of the security device. This solves the technical problem of being unable to determine the usage state of a security device and achieves the technical effect of determining the usage state of a security device. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 is a flowchart of a method for determining the state of a security device according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a method for intelligent tracking, photographing and recognizing the safety belt wearing status of workers in high-altitude operation scenarios according to an embodiment of the present invention.

[0021] Figure 3 is a schematic diagram of an intelligent tracking and photo recognition system for the wearing status of safety belts of workers in high-altitude operation scenarios according to an embodiment of the present invention.

[0022] Figure 4 is a schematic diagram of a device for determining the state of a safety device according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "comprising" and "having" and any variations thereof in this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, functional component, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, functional components, or devices.

[0025] According to an embodiment of the present invention, an embodiment of a method for determining the state of a security device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 is a flowchart of a method for determining the state of a security device according to an embodiment of the present invention. As shown in Figure 1, the method may include the following steps:

[0027] Step S101: Obtain scene images and target features of the security device.

[0028] In the technical solution provided in step S101 of the present invention, the scene image is used to indicate the usage scenario of the safety device, and the target feature is used to indicate the appearance and / or status characteristics of the safety device. The target feature may include features such as color, shape, and texture, and the safety device may be at least a seatbelt and a safety rope.

[0029] In this embodiment, scene images and target features of the security device are acquired. For example, scene images are acquired through a camera, and target features are acquired through a pre-defined database. This is merely an example and does not limit the specific method for acquiring scene images and target features of the security device.

[0030] For example, cameras can be mounted in locations that effectively capture images of workers at height, such as fixed supports or mobile robotic arms around the work area. These cameras feature high resolution (e.g., 4K and above), automatic focus adjustment, and a wide field of view to ensure clear images of the worker's entire body and critical areas. The cameras also incorporate advanced image sensors that sensitively detect changes in light, providing a basis for light-based image capture, thereby allowing the camera to capture scene images of safety equipment.

[0031] For example, for a seatbelt, the target feature can be color (such as orange, yellow, etc.) segmented within a specific range in the HSV (Hue Saturation Value) color space. For a safety rope, the target features can be thickness, color (which may differ from that of a seatbelt), and texture features.

[0032] Step S102: Identify the target region from the scene image based on the target features.

[0033] In the technical solution provided by step S102 of the present invention, the target area includes the area where the security device is located.

[0034] In this embodiment, after obtaining the target features of the security device in step S101, the target region is identified from the scene image based on the target features. For example, the target region can be identified from the scene image based on the target features using methods such as Hough transform. This is only an example and does not limit the specific method for identifying the target region.

[0035] For example, for seat belts, a specific range can be defined in the HSV color space based on their common colors (such as orange, yellow, etc.) for color segmentation. Simultaneously, methods such as Hough transform are used to detect elongated shapes, and the periodicity of the seat belt's texture is combined to further confirm the seat belt area, i.e., the target area.

[0036] Step S103: Determine the target status of the security device based on the target area.

[0037] In the technical solution provided by step S103 of the present invention, the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state.

[0038] In this embodiment, after identifying the target region from the scene image in step S102, the target state of the security device is determined based on the target region. For example, a machine learning algorithm may be used to determine the target state of the security device. This is merely an example and does not limit the specific method for determining the target state of the security device.

[0039] For example, machine learning algorithms trained on a large amount of labeled data (such as CNNs) are used to identify the status of safety equipment in the target area to ensure accurate judgment of the wearing status of seat belts and safety ropes.

[0040] It should be noted that the above embodiments can be implemented using an intelligent tracking and photography system for identifying safety belts used in high-altitude operations.

[0041] In steps S101 to S103 of this invention, a scene image and target features of the safety device are acquired. The scene image indicates the usage scenario of the safety device, and the target features indicate the appearance and / or status characteristics of the safety device. Based on the target features, a target region is identified from the scene image, wherein the target region includes the area where the safety device is located. Based on the target region, the target state of the safety device is determined, wherein the target state indicates whether the safety device is in a safe wearing state or not. In other words, this invention analyzes the acquired scene image and target features of the safety device to determine its target state, achieving the purpose of real-time monitoring of the safety device. This solves the technical problem of being unable to determine the usage state of the safety device and achieves the technical effect of determining the usage state of the safety device.

[0042] The method described in this embodiment will be further described below.

[0043] As an optional embodiment, acquiring scene images of a security device includes: acquiring environmental information of the security device, wherein the environmental information is used to indicate the weather and / or lighting environment in which the security device is located; adjusting the acquisition angle of the acquisition device based on the environmental information; and controlling the acquisition device to acquire scene images of the security device according to the acquisition angle.

[0044] In this embodiment, environmental information of the security device is acquired. For example, environmental information of the security device is acquired through a photosensor.

[0045] For example, a photosensitive sensor continuously monitors ambient light. When workers are working at height, changes in their position can alter light reflection and obstruction. The photosensitive sensor can sensitively detect these changes in light. When the magnitude of the light change exceeds a set threshold, it immediately sends a trigger signal to the system. After receiving the trigger signal, the intelligent tracking and photography system activates the high-definition camera's photography function to photograph workers wearing safety equipment, thereby obtaining environmental information about the safety equipment. Let the initial light intensity be I0 and the current light intensity be I. The formula for calculating the magnitude of the light change is shown in the following formula (1): △I=|I-I0|; (1)

[0046] Where △I is the amplitude of light change, and T is the set threshold for light change. When △I≥T, a photo is triggered.

[0047] Optionally, the acquisition angle of the acquisition device can be adjusted according to environmental information; thereby acquiring scene images of the security device according to the acquisition angle. The acquisition device can be a camera.

[0048] For example, cameras utilize human recognition technology to quickly determine the position and outline of workers through image analysis. Deep learning-based human detection algorithms can be employed to accurately identify the position of the human body in the image. Based on the human position information, the acquisition angle is determined, and the camera automatically adjusts its focus accordingly to ensure a clear image of the worker's body, especially the installation position of the safety belt and safety rope around their waist—that is, to capture a scene image of the safety equipment. For instance, the lens assembly can be driven by a motor to adjust the focus to achieve optimal imaging results, and the camera exposure time t... exp The aperture size f can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The appropriate exposure time can be calculated using the following formula (2):

[0049] Where N is the camera's ISO sensitivity and I is the ambient light intensity.

[0050] As an optional embodiment, identifying a target region from a scene image based on target features includes: segmenting the scene image to obtain multiple sub-scene images; extracting scene features from each sub-scene image; and determining the region of the sub-scene image corresponding to the scene features as the target region in response to matching the scene features with the target features.

[0051] In this embodiment, the scene image is segmented to obtain multiple sub-scene images. For example, the scene image can be segmented using methods such as neural networks, and pixels with similarity can be grouped into the same sub-scene image.

[0052] Optionally, after obtaining multiple sub-scene images, scene features are extracted from each sub-scene image, and the scene features of each sub-scene image are matched with target features. When a scene feature matches a target feature, it indicates that the scene feature is similar to the target feature. That is, there is a security device in the sub-scene image corresponding to the scene feature that matches the target feature. Based on this, the region of the sub-scene image corresponding to the scene feature can be determined as the target region.

[0053] As an optional implementation method, determining the target state of a security device based on target features includes: inputting the target features into a recognition model for analysis to obtain the target state of the security device.

[0054] In this embodiment, the target features are input into the recognition model for analysis to obtain the target state of the security device. The recognition model can be a machine learning algorithm trained with a large amount of labeled data.

[0055] Optionally, by analyzing the target features through an identification model, it can be determined whether the safety device is not being worn or is being worn safely, thereby achieving the purpose of monitoring the safety device.

[0056] As an optional embodiment, the target features are input into the recognition model for analysis to obtain the target state of the safety device, including: comparing the target features with preset features to obtain a comparison result, wherein the preset features are used to indicate the appearance features and / or state features of the safety device in a safe wearing state; in response to the comparison result that the target features match the preset features, the target state is determined to be a safe wearing state.

[0057] In this embodiment, the target feature is compared with a preset feature to obtain a comparison result. The preset feature can be, for example, a pre-defined texture feature of the seatbelt or a color feature of the safety rope; this is merely an example and does not limit the specific content of the preset feature.

[0058] Optionally, after determining the comparison result, if the comparison result shows that the target feature matches the preset feature, it means that the state of the safety device corresponding to the target feature is consistent with the safe wearing state of the safety device corresponding to the preset feature. Based on this, the target state can be determined to be the safe wearing state.

[0059] As an optional embodiment, the method for determining the state of a security device further includes: preprocessing the scene image.

[0060] In this embodiment, the scene image is preprocessed, and the preprocessing may include at least operations such as grayscale conversion and noise reduction.

[0061] For example, for grayscale processing, a weighted average method is used to convert a color image into a grayscale image. For example, for an RGB color image, R represents red, G represents green, and B represents blue. The grayscale value G of each pixel can be obtained by the following formula (3): G = 0.299R + 0.587G + 0.114B. (3)

[0062] For noise reduction, methods such as Gaussian filtering are used to remove noise from the image. Gaussian filtering works by weighting each pixel in the image and its neighboring pixels, with the weights determined by a Gaussian function. Let the image be I(x,y), and the image after Gaussian filtering be I... filtered The formula for calculating (x,y) can be expressed using the following formula (4):

[0063] Where x and y represent the coordinates of the specified target pixel in the two-dimensional plane of the image, used to determine the location where the filtered pixel value is to be calculated; ξ and η are both integration variables, traversing all coordinate points on the original image plane to perform weighted processing on the entire image; e is the natural constant, the base of the exponential function, and σ is the standard deviation of the Gaussian distribution, used to control the shape of the Gaussian function. A larger σ value results in a flatter Gaussian function curve, meaning a wider neighborhood range participating in the weighted average, and a higher degree of image smoothness; a smaller σ value results in a steeper curve, a relatively smaller neighborhood range participating in the weighted average, and more image details are preserved. 2πσ 2 The coefficient part is used to normalize the weighted summation result. π represents pi, which is approximately 3.14159. dξdη represents the integral elements of variables ξ and η when performing integral operations on the original image, used for cumulative weighted summation over the entire image plane.

[0064] It should be noted that the above embodiments can be implemented using an intelligent tracking and photography system for identifying safety belts used in high-altitude operations.

[0065] In this embodiment, a scene image and target features of the security device are acquired. The scene image indicates the usage scenario of the security device, and the target features indicate the appearance and / or status characteristics of the security device. Based on the target features, a target region is identified from the scene image, wherein the target region includes the area where the security device is located. Based on the target region, the target state of the security device is determined, wherein the target state indicates whether the security device is in a safe wearing state or not wearing a safe wearing state. In other words, this invention analyzes the acquired scene image and target features of the security device to determine its target state, achieving the purpose of real-time monitoring of the security device. This solves the technical problem of being unable to determine the usage state of the security device and achieves the technical effect of determining the usage state of the security device.

[0066] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0067] Currently, safety belts are crucial equipment for protecting the lives of workers engaged in high-altitude operations. They serve as the last line of defense for worker safety. Monitoring and determining the status of safety equipment is a critical step in ensuring worker safety.

[0068] In related technologies, due to various reasons, workers may unexpectedly lose the protection of safety equipment during operations, posing a significant safety risk. Currently, there is a lack of a method to promptly and accurately determine whether workers at height have lost the protection of their safety equipment. Therefore, there is a technical problem of being unable to determine the operational status of safety equipment.

[0069] There is currently no effective solution to the aforementioned technical problem of being unable to determine the usage status of safety equipment.

[0070] However, this invention proposes a method for intelligently tracking and photographing the wearing status of safety belts of workers in high-altitude operation scenarios. The method automatically triggers photography through light recognition, automatically focuses based on the position of the human body, and accurately identifies the installation position of the safety belt and safety rope. It can promptly detect incorrect wearing and issue an alarm, thus solving the technical problem of not being able to determine the usage status of safety equipment and achieving the technical effect of determining the usage status of safety equipment.

[0071] The embodiments of the present invention will be further described below.

[0072] Figure 2 is a flowchart of a method for intelligent tracking and image recognition of the safety belt wearing status of workers in high-altitude operation scenarios according to an embodiment of the present invention. The recognition method includes the following steps:

[0073] Step S201: Acquire an image of the security device and perform preprocessing.

[0074] In this embodiment, a photosensitive sensor continuously monitors ambient light. When workers are working at height, changes in their position can alter light reflection and obstruction. The photosensitive sensor can sensitively detect these changes in light. When the magnitude of the light change exceeds a set threshold, a trigger signal is immediately sent to the system. Upon receiving the trigger signal, the intelligent tracking and photography system activates the high-definition camera's photography function to capture images of workers wearing safety equipment, thereby obtaining environmental information about the safety equipment. Let the initial light intensity be I0 and the current light intensity be I. The formula for calculating the magnitude of the light change is as shown in the aforementioned formula (1), and will not be repeated here.

[0075] Optionally, the camera utilizes human body recognition technology to quickly determine the position and outline of the worker through image analysis. A deep learning-based human detection algorithm can be employed to accurately identify the human body's position in the image. Based on the human body's position information, the acquisition angle is determined, and the camera automatically adjusts its focus accordingly to ensure clear imaging of the worker's body, especially the installation position of the safety belt and safety rope around their waist—that is, to capture a scene image of the safety equipment. For example, the focus can be adjusted by a motor-driven lens assembly to achieve optimal imaging results, and the camera exposure time t... exp The aperture size f can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The appropriate exposure time can be calculated using the aforementioned formula (2), which will not be elaborated here.

[0076] Optionally, upon receiving the trigger signal, the camera's shooting function is activated, and the camera takes a picture according to preset parameters (such as exposure time, aperture size, etc.). The camera's exposure time and aperture size can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The appropriate exposure time can be calculated using the following formula (5):

[0077] Where t is the exposure time, k is a constant, S is the camera's ISO, and I is the ambient light intensity.

[0078] Optionally, for grayscale processing, a weighted average method is used to convert the color image into a grayscale image. For example, for an RGB color image, the grayscale value G of each pixel can be obtained by the aforementioned formula (3), which will not be elaborated here.

[0079] Optionally, for noise reduction, methods such as Gaussian filtering can be used to remove noise from the image. Gaussian filtering works by weighting each pixel in the image and its neighboring pixels, with the weights determined by a Gaussian function. Let the image be I(x,y), and the image after Gaussian filtering be I... filtered The formula for calculating (x,y) can be expressed using the aforementioned formula (4), which will not be repeated here.

[0080] Step S202: Identify the seat belt and safety rope from the preprocessed image.

[0081] In this embodiment, feature extraction involves the image processor preprocessing the captured image, including operations such as grayscale conversion and noise reduction. Then, features such as color, shape, and texture are used to extract areas that may contain seatbelts and safety ropes.

[0082] Optionally, for seat belts, a specific range can be defined in the HSV color space for color segmentation based on their common colors (such as orange, yellow, etc.). Simultaneously, methods such as Hough transform are used to detect elongated shapes, and the seat belt's texture periodicity is combined to further confirm the seat belt region.

[0083] Alternatively, a safety rope can be identified by its specific thickness, color (which may differ from a seatbelt), and texture characteristics. For example, a safety rope is typically thinner than a seatbelt and may be a specific color such as blue or gray.

[0084] Optionally, color features can be used to segment the image based on the specific color of the safety rope (such as blue or gray). An HSV range for the corresponding color is set to extract areas that are likely to be safety ropes.

[0085] Optionally, for thickness characteristics, edge detection algorithms (such as Canny edge detection) are used to detect edges in the image, and then the width of the edges is analyzed to determine areas that may be protected ropes. Protective ropes are typically thinner than seat belts, and a width threshold is set to filter out areas that match the thickness characteristics of the protective rope. Let the edge width be w, and W be the protective rope width threshold; when w ≤ W, it is considered a possible protective rope area.

[0086] Step S203: Determine the wearing status of the safety belt and safety rope.

[0087] In this embodiment, feature analysis is performed on the extracted areas of the safety belt and safety rope, and compared with pre-stored correct wearing patterns. This determines whether the safety belt is wrapped around the worker's body and properly fastened, and whether the safety rope is connected in the appropriate position and is loose. A machine learning algorithm (such as CNN) trained on a large amount of labeled data is used for identification to ensure accurate judgment of the wearing status of the safety belt and safety rope.

[0088] Optionally, a large image dataset containing both correctly worn and unworn seat belts and safety ropes is collected. This dataset is divided into training, validation, and test sets. The CNN model is trained using the training set, and the model's weights are adjusted via backpropagation to achieve good accuracy on the validation set.

[0089] Optionally, the extracted images of the seat belt and safety rope area are input into the trained CNN model, and the model outputs the classification results of the wearing status of the seat belt and safety rope, such as wearing correctly, wearing incorrectly, or not wearing.

[0090] Step S204, Alarm and data transmission.

[0091] In this embodiment, if the image processor determines that the worker is not wearing a safety belt or safety rope correctly, the system immediately triggers an alarm. The alarm can be an audible alarm, emitting a loud sound through a speaker installed at the work site, or a tactile alarm, sending information about the incorrect wearing to a receiving device carried by the on-site supervisor, such as a mobile phone or a dedicated monitoring terminal.

[0092] Optionally, for data transmission, the system transmits image data, location information (if applicable), and timestamps of workers not wearing the correct protective gear to the monitoring center or the terminal devices of management personnel. This data can be used for subsequent safety recording, accountability, etc.

[0093] Optionally, when the tracking and photography system detects that a worker is not wearing a safety belt, the control module will, in addition to triggering an alarm, send a signal to the light-emitting device to make it emit a specific alarm light source. For example, the light-emitting element can emit a rapidly flashing red light, or switch to a color and frequency that is significantly different from the normal light-emitting mode, in order to attract the attention of the worker and those around them.

[0094] Optionally, when the system determines that a worker is not wearing a safety belt or safety rope correctly, it triggers an alarm and emits a loud alarm sound through a speaker. The speaker can be installed in a conspicuous location at the work site, such as the edge of the work platform or at the entrance, so that workers can hear the alarm promptly. At the same time, the volume and pitch of the speaker should be adjusted to ensure clear audibility in noisy work environments.

[0095] Optionally, information regarding incorrect wearing of protective gear can be sent to receiving devices carried by on-site supervisors, such as mobile phones or dedicated monitoring terminals. These receiving devices can connect to the system via a wireless network to receive alarm information and image data in real time. Supervisors can use these devices to check the wearing status of workers and take timely corrective measures. Simultaneously, the receiving devices can be configured with alert functions, such as vibration and sound prompts, to ensure supervisors notice alarm information promptly.

[0096] Figure 3 is a schematic diagram of an intelligent tracking and photo recognition system for detecting the wearing of safety belts by workers in high-altitude work scenarios according to an embodiment of the present invention. The intelligent tracking and photo recognition system 300 includes: a high-definition camera 301, an image processor 302, a storage device 303, and a photosensor 304.

[0097] High-definition camera 301: Installed in a location that effectively captures images of workers at height, such as a fixed support or a movable robotic arm around the work area. The camera features high resolution (e.g., 4K and above), automatic focus adjustment, and a wide field of view to ensure clear capture of the worker's entire body and key areas. The camera incorporates an advanced image sensor that sensitively detects changes in light, providing the basis for light-triggered image capture.

[0098] Image processor 302: Connects to a high-definition camera to receive image data and perform real-time processing and analysis. It employs a high-performance DSP (Digital Signal Processor) or GPU (Graphics Processing Unit) to ensure fast data processing speed.

[0099] Storage device 303: Used to store raw image data and analysis results. A high-capacity SSD (Solid State Drive) or flash memory card can be selected, supporting fast read and write operations.

[0100] Photosensor 304: Installed near or integrated with the camera, it senses the intensity and changes in ambient light. When the light change meets preset conditions, it triggers the camera to take a picture.

[0101] In this embodiment, the high-definition camera 301 is mounted using a fixed bracket, such as a sturdy metal bracket, which is fixed to the building structure or the edge of the work platform by expansion bolts or welding. Ensure the bracket can withstand the weight of the camera and potential external forces such as wind. During installation, adjust the camera's angle and height to capture the workers from the optimal viewing angle.

[0102] Optionally, the HD camera 301 parameters, such as resolution, can be selected to achieve 4K or higher resolution, depending on actual needs, to ensure clear capture of the safety belt and rope installation positions of workers. In scenarios requiring high image quality, a higher resolution camera can be selected to maintain clarity even when the image is magnified for analysis.

[0103] Automatic focus adjustment range: Set an appropriate automatic focus adjustment range based on the size of the work area and the operator's movement range. If the work area is large, a larger focus adjustment range can be selected to ensure clear imaging at different distances. Simultaneously, adjust the focus adjustment speed and precision according to the actual situation to ensure fast and accurate focusing.

[0104] Optionally, the sensitivity of the image sensor can be adjusted according to the lighting conditions at the work site. In low-light environments, the sensitivity of the image sensor can be increased to ensure that clear images can be captured. However, excessively high sensitivity may lead to increased image noise, so a balance needs to be struck between sensitivity and image quality.

[0105] Optionally, a movable robotic arm can be installed: the robotic arm can be installed on large equipment such as tower cranes or hoists using bolt or bayonet connections. During installation, it is essential to ensure that the robotic arm's range of motion covers the entire work area and can stably maintain the position and orientation of the camera.

[0106] Optionally, the photosensor 304: If integrated with the camera, it can be installed directly along with the camera's mounting method. If installed independently, it can be fixed in a suitable position near the camera using a small bracket or glue, ensuring that the photosensitive surface of the photosensor faces the work area to accurately detect changes in light.

[0107] Optionally, the light change threshold can be set appropriately based on the light changes at the work site and the activity characteristics of the workers. If the light changes frequently at the work site, the threshold can be appropriately lowered to improve the system's sensitivity. However, an excessively low threshold may lead to false triggers, so a balance needs to be struck between sensitivity and accuracy.

[0108] Optionally, the response time can be adjusted to allow the photosensor to quickly detect changes in light and trigger the capture. A response time that is too short may cause the system to trigger frequently, wasting resources; a response time that is too long may cause critical capture opportunities to be missed. Therefore, adjustments need to be made based on actual conditions to ensure the system can respond promptly to changes in light.

[0109] Optionally, the image processor 302 and storage device 303: the control box can be placed on a stable surface or mounted on a wall. The devices inside the control box should be arranged and secured properly to prevent loosening and damage during transportation and use. Cables connecting the various devices should be neatly routed and secured to avoid tangled cables that could affect the normal operation of the equipment.

[0110] Optionally, the image processor 302 parameter, data processing speed, should be set appropriately based on the camera's resolution and frame rate, as well as the system's real-time requirements. If real-time processing of large amounts of image data is required, a high-performance image processor can be selected, and its parameters adjusted to improve processing speed. For example, the number of processor cores can be increased, the clock frequency increased, or the algorithm optimized.

[0111] Optionally, feature extraction parameters: For feature extraction of seat belts and safety ropes, appropriate parameters need to be set according to the actual situation. For example, for color segmentation, the range of the HSV color space can be adjusted according to the actual colors of the seat belts and safety ropes. For shape and texture feature extraction, the corresponding algorithm parameters can be adjusted according to the shape and texture characteristics of the seat belts and safety ropes to improve the accuracy of feature extraction.

[0112] Optionally, storage device parameters 303, such as storage capacity, can be selected based on the job time and the size of the image data. If the job time is long or a large amount of image data needs to be stored, a large-capacity solid-state drive or flash memory card can be selected. At the same time, the read and write speeds of the storage device should be considered to ensure fast storage and retrieval of image data.

[0113] Optionally, data retention time: The retention time for image data is set according to security management requirements. Generally, image data needs to be retained for a certain period for viewing and analysis when needed. However, excessively long retention times may consume a large amount of storage space, so a balance needs to be struck between storage space and data retention time.

[0114] In this embodiment, the system automatically triggers photography through light recognition, automatically focuses based on the human body position, and accurately identifies the installation positions of the safety belt and safety rope. It promptly detects cases of incorrect wearing and issues an alarm, thus solving the technical problem of being unable to determine the usage status of safety equipment and achieving the technical effect of determining the usage status of safety equipment.

[0115] According to embodiments of the present invention, a device for determining the state of a safety device is also provided. It should be noted that this device for determining the state of a safety device can be used to execute the method for determining the state of a safety device in the method embodiments.

[0116] Figure 4 is a schematic diagram of a device for determining the state of a security device according to an embodiment of the present invention. As shown in Figure 4, the device 400 for determining the state of the security device may include: an acquisition unit 401, an identification unit 402, and a determination unit 403.

[0117] The acquisition unit 401 is used to acquire scene images and target features of the security device, wherein the scene images are used to indicate the usage scenario of the security device, and the target features are used to indicate the appearance features and / or status features of the security device.

[0118] The recognition unit 402 is used to identify a target region from a scene image based on target features, wherein the target region includes the area where the security device is located.

[0119] The determining unit 403 is used to determine the target state of the safety device based on the target area, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state.

[0120] Optionally, the acquisition unit 401 may include: an acquisition module for acquiring environmental information of the security device, wherein the environmental information is used to indicate the weather and / or lighting environment in which the security device is located; an adjustment module for adjusting the acquisition angle of the acquisition device based on the environmental information; and an acquisition module for controlling the acquisition device to acquire scene images of the security device according to the acquisition angle.

[0121] Optionally, the recognition unit 402 may include: a segmentation module for segmenting the scene image to obtain multiple sub-scene images; an extraction module for extracting scene features from each sub-scene image; and a matching module for determining the region of the sub-scene image corresponding to the scene features as the target region in response to the matching of scene features and target features.

[0122] Optionally, the determining unit 403 further includes an analysis module for inputting target features into the recognition model for analysis to obtain the target state of the security device.

[0123] Optionally, the analysis module may include: a comparison submodule, used to compare the target feature with preset features to obtain a comparison result, wherein the preset features are used to indicate the appearance features and / or state features of a pre-defined safety device in a safe wearing state; and a matching submodule, used to determine the target state as a safe wearing state in response to the comparison result indicating that the target feature matches the preset feature.

[0124] Optionally, the safety device status determination device 400 may further include: a preprocessing unit for preprocessing the scene image.

[0125] In this embodiment, a scene image and target features of the security device are acquired. The scene image indicates the usage scenario of the security device, and the target features indicate the appearance and / or status characteristics of the security device. Based on the target features, a target region is identified from the scene image, wherein the target region includes the area where the security device is located. Based on the target region, the target state of the security device is determined, wherein the target state indicates whether the security device is in a safe wearing state or not wearing a safe wearing state. In other words, this invention analyzes the acquired scene image and target features of the security device to determine its target state, achieving the purpose of real-time monitoring of the security device. This solves the technical problem of being unable to determine the usage state of the security device and achieves the technical effect of determining the usage state of the security device.

[0126] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program execution method embodiment is a method for determining the state of a security device.

[0127] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes a method for determining the state of a security device in a method embodiment.

[0128] According to an embodiment of the present invention, a computer program product is also provided, the computer program product including computer instructions, which, when executed by a processor, implement the method for determining the state of a security device in the method embodiment.

[0129] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0130] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent functional component, 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 all or part of the technical solution, can be embodied in the form of a software functional component. This computer software functional component is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0135] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the state of a safety device, characterized in that, include: Acquire scene images and target features of a security device, wherein the scene images are used to indicate the usage scenario of the security device, and the target features are used to indicate the appearance and / or status features of the security device; Based on the target features, a target region is identified from the scene image, wherein the target region includes the area where the security device is located; Based on the target area, the target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state.

2. The method according to claim 1, characterized in that, Acquire scene images of the security devices, including: Obtain environmental information of the safety device, wherein the environmental information is used to indicate the weather and / or lighting conditions in which the safety device is located; Based on the environmental information, adjust the acquisition angle of the acquisition device; The acquisition device is controlled to acquire the scene image of the security device according to the acquisition angle.

3. The method according to claim 1, characterized in that, Based on the target features, the target region is identified from the scene image, including: The scene image is segmented to obtain multiple sub-scene images; Extract scene features from each of the sub-scene images; In response to the scene feature matching the target feature, the region of the sub-scene image corresponding to the scene feature is determined as the target region.

4. The method according to claim 1, characterized in that, Based on the target characteristics, determining the target state of the security device includes: The target features are input into the recognition model for analysis to obtain the target state of the security device.

5. The method according to claim 4, characterized in that, The target features are input into the recognition model for analysis to obtain the target state of the security device, including: The target feature is compared with the preset feature to obtain a comparison result, wherein the preset feature is used to indicate the appearance feature and / or state feature of the safety device in the safe wearing state as preset; In response to the comparison result indicating that the target feature matches the preset feature, the target state is determined to be the safe wearing state.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The scene image is preprocessed.

7. A device for determining the state of a safety device, characterized in that, include: An acquisition unit is used to acquire a scene image and target features of a security device, wherein the scene image is used to indicate the usage scenario of the security device, and the target features are used to indicate the appearance features and / or status features of the security device; The identification unit is configured to identify a target region from the scene image based on the target features, wherein the target region includes the area where the security device is located; A determining unit is configured to determine the target state of the safety device based on the target area, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium is located to perform the method of any one of claims 1 to 6.

9. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when it runs.

10. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a processor, implement the method described in any one of claims 1 to 6.

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