Identifier analysis system using image processing
By collecting a number of parameters and image information of the camera institution, the existence of BP neural network recognition image processing components is used to solve the problem of real information caused by image processing, and the accuracy and reliability of image analysis are realized.
Patent Information
- Application Number
- CN202411467491.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-21
- Publication Date
- 2025-01-24
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the prior art, images transmitted to the image analyzing party are often processed through image processing, resulting in the original picture content being optimized or the details being obscured, making it difficult to obtain real image content.
By collecting a number of camera parameters of targeted camera institutions and multiple image information of instant collection images, the BP neural network intelligent recognition camera in the BP neural network after each training movement has an image processing component Sensor's direct transmission image.
Provide real information for subsequent monitoring results to ensure the accuracy and reliability of image analysis.
Smart Images
Figure CN119359983A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of image processing, and in particular to a marking analysis system using image processing. Background Art
[0002] Generally, image processing techniques improve the interpretability of images by identifying key features or reading text information in images in order to classify or detect objects present in the image. For example, when people resize an image, they can change the height or width of the image, or change both the height and width while keeping the aspect ratio unchanged. The aspect ratio of an image is the ratio of the width to the height of the image. And for example, grayscale images are often used to identify the edges of target objects because grayscale images not only help understand the contrast and shadow gradients in the image, but also help understand the image features. Compared with the 2D channels of grayscale images, RGB images have three channels: red, green, and blue. Compared with color images, grayscale images have less information per pixel, so the processing time of grayscale images will be faster.
[0003] In the prior art, the direct-input image of the photoelectric sensor unit of the camera mechanism is the most realistic picture content reflecting the current shooting scene of the camera mechanism, and is also the image content that the image analysis party most hopes to obtain. However, the image transmitted to the image analysis party is often the picture content that has undergone some image processing inside or outside the camera mechanism. Although the image processing optimizes the original picture content to a certain extent, it also conceals some details of the original picture content. Summary of the invention
[0004] In order to solve the technical problems in the related fields, the present invention provides an identification analysis system using image processing, which collects multiple camera parameters of a targeted camera mechanism and multiple image information of an instant collected image, and intelligently identifies whether there is an image processing component inside the targeted camera mechanism, that is, whether the instant collected image is a direct input image of a photoelectric sensing unit of the targeted camera mechanism based on a BP neural network after completing each training action, thereby providing real information for the judgment of subsequent monitoring results, using the ISO value, exposure time and dynamic range of grayscale values of the targeted camera mechanism as multiple camera parameters of the targeted camera mechanism, and using the number of main objects, foreground area ratio and maximum target area ratio of the instant collected image as multiple image information of the instant collected image, and using different customized structures for different targeted camera mechanisms after completing each training action. In the BP neural network after completing each training action, the number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component of the targeted camera mechanism, thereby ensuring the stability and effectiveness of the intelligent recognition result.
[0005] According to the present invention, there is provided a system for analyzing identification using image processing, the system comprising: A targeted camera mechanism, including a camera lens, a gun-type housing, a flexible circuit board, a filter, and a photoelectric sensing component, is used to perform image acquisition on the scene in front of the camera lens to obtain and output a corresponding real-time acquisition image; A content learning mechanism, used for training the BP neural network for each time to obtain the BP neural network after completing each training action, and outputting the BP neural network after completing each training action, wherein the number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component; A continuous mapping device is connected to the targeted camera mechanism and the content learning mechanism, respectively, and is used to collect multiple camera parameters of the targeted camera mechanism and multiple image information of the real-time collected images, and based on the BP neural network after completing each training action, intelligently identifies whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images; A wireless transmission device, connected to the continuous mapping device, is used to intelligently identify that there is no image processing component inside the targeted camera mechanism, and wirelessly transmit the image identification of the instant captured image marked as a RAW image to a remote big data server; Wherein, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected images, and intelligently identifying whether there is an image processing component inside the targeted camera mechanism based on the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images based on the BP neural network after completing each training action includes: taking the ISO value, exposure time and dynamic range of grayscale value of the targeted camera mechanism as the multiple camera parameters of the targeted camera mechanism; Among them, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected images, and based on the BP neural network after completing each training action, intelligently identifying whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images also includes: taking the number of main objects, foreground area ratio and maximum target area ratio of the real-time collected images as multiple image information of the real-time collected images.
[0006] It can be seen that the present invention has at least the following significant substantive features: Substantive feature A: Collecting multiple camera parameters of the targeted camera mechanism and multiple image information of the real-time collected images, based on the BP neural network after completing each training action, intelligently identifying whether there is an image processing component inside the targeted camera mechanism, that is, whether the real-time collected image is a direct input image of the photoelectric sensor unit of the targeted camera mechanism, thereby providing real information for the judgment of subsequent monitoring results; Substantive feature B: using the ISO value, exposure time and dynamic range of grayscale values of the targeted camera mechanism as multiple camera parameters of the targeted camera mechanism, and using the number of main objects, foreground area ratio and maximum target area ratio of the real-time captured image as multiple image information of the real-time captured image; Substantive Feature C: A BP neural network with different customized structures is used for different targeted camera mechanisms after completing each training action. In the BP neural network after completing each training action, the number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component of the targeted camera mechanism, thereby ensuring the stability and effectiveness of the intelligent recognition results.
[0007] The identification analysis system using image processing of the present invention is easy to operate and widely used. Since it can intelligently identify whether there is an image processing component inside the targeted camera mechanism based on multiple camera parameters of the collected targeted camera mechanism and multiple image information of the real-time collected image, that is, whether the real-time collected image is a direct input image of the photoelectric sensor unit of the targeted camera mechanism, it provides real information for the judgment of subsequent monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 FIG. 1 is a diagram showing the internal structure of a marking analysis system using image processing according to a first embodiment of the present invention.
[0009] Figure 2 FIG. 1 is a diagram showing the internal structure of a marking analysis system using image processing according to a second embodiment of the present invention.
[0010] Figure 3 FIG. 1 is a diagram showing the internal structure of a marking analysis system using image processing according to a third embodiment of the present invention. DETAILED DESCRIPTION
[0011] An embodiment of a logo analysis system using image processing according to the present invention will be described in detail below with reference to the accompanying drawings.
[0012] Figure 1 This is an internal structural diagram of a logo analysis system using image processing according to a first embodiment of the present invention, wherein the system comprises: A targeted camera mechanism, including a camera lens, a gun-type housing, a flexible circuit board, a filter, and a photoelectric sensing component, is used to perform image acquisition on the scene in front of the camera lens to obtain and output a corresponding real-time acquisition image; For example, a targeted camera mechanism includes a camera lens, a gun-type housing, a flexible circuit board, a filter, and a photoelectric sensing component, and is used to perform image acquisition on a scene in front of the camera lens to obtain and output a corresponding real-time acquisition image, including: in the targeted camera mechanism, the filter is arranged between the camera lens and the photoelectric sensing component; For example, a targeted camera mechanism includes a camera lens, a gun-type housing, a flexible circuit board, a filter, and a photoelectric sensing component, and is used to perform image acquisition on a scene in front of the camera lens to obtain and output a corresponding real-time acquisition image, and further includes: the camera lens is arranged at the front end of the gun-type housing; A content learning mechanism, used for training the BP neural network for each time to obtain the BP neural network after completing each training action, and outputting the BP neural network after completing each training action, wherein the number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component; A continuous mapping device is connected to the targeted camera mechanism and the content learning mechanism, respectively, and is used to collect multiple camera parameters of the targeted camera mechanism and multiple image information of the real-time collected images, and based on the BP neural network after completing each training action, intelligently identifies whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images; A wireless transmission device, connected to the continuous mapping device, is used to intelligently identify that there is no image processing component inside the targeted camera mechanism, and wirelessly transmit the image identification of the instant captured image marked as a RAW image to a remote big data server; Wherein, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected images, and intelligently identifying whether there is an image processing component inside the targeted camera mechanism based on the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images based on the BP neural network after completing each training action includes: taking the ISO value, exposure time and dynamic range of grayscale value of the targeted camera mechanism as the multiple camera parameters of the targeted camera mechanism; Wherein, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected image, and based on the BP neural network after completing each training action, intelligently identifying whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected image, further includes: taking the number of main objects, the proportion of foreground area and the proportion of maximum target area of the real-time collected image as multiple image information of the real-time collected image; And wherein, the wireless transmission device is also used to intelligently identify the existence of an image processing component inside the targeted camera mechanism, and wirelessly transmit the image identification of the real-time captured image marked as a non-RAW image to a remote big data server.
[0013] Figure 2 FIG. 1 is a diagram showing the internal structure of a marking analysis system using image processing according to a second embodiment of the present invention.
[0014] exist Figure 2 In, with Figure 1 Differently, the identification analysis system using image processing shown in the second embodiment of the present invention may further include: An on-site storage device, connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, respectively, for providing data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner; The on-site storage device is respectively connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, and is used to provide data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner, including: the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device are respectively connected to the on-site storage device using different data channels; Among them, the on-site storage device is respectively connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, and is used to provide data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner, including: the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device use the same reference clock signal.
[0015] Figure 3 FIG. 1 is a diagram showing the internal structure of a marking analysis system using image processing according to a third embodiment of the present invention.
[0016] exist Figure 3 In, with Figure 1 Differently, the identification analysis system using image processing shown in the third embodiment of the present invention may also include: A content display device, connected to the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device, respectively, for simultaneously displaying various status information of the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device; Wherein, the content display device is connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device respectively, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, including: the various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device displayed simultaneously includes the sleep state or working state of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device; Wherein, the content display device is respectively connected to the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device, and further includes: the content display device is a liquid crystal display screen or an LED display array; Wherein, the content display device is respectively connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and further comprises: the LED display array is composed of a plurality of LED display units arranged in a rectangular matrix pattern; And wherein, the content display device is respectively connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and also includes: the structure of the multiple LED display units arranged in a rectangular matrix pattern is the same.
[0017] In addition, in the identification analysis system applying image processing, the content learning mechanism is used to train the BP neural network for each time to obtain the BP neural network after completing each training action, and output the BP neural network after completing each training action. The number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component, including: using a numerical mapping formula to represent the numerical mapping relationship of the monotonically positive correlation between the number of times the BP neural network completes the training action and the number of pixel circuits in the photoelectric sensing component.
[0018] Although only exemplary embodiments of the present invention have been described in detail above, those skilled in the art will readily appreciate that many variations are possible in these exemplary embodiments without materially departing from the novel principles and advantages of the present invention. Accordingly, all such variations are intended to be included within the scope of the present invention as defined by the following claims. In the claims, means and function clauses are intended to include structures described herein as performing the recited functions, and include not only structural equivalents, but also equivalent structures.
Claims
1. A logo analysis system using image processing, characterized in that: The system comprises: A targeted camera mechanism, including a camera lens, a gun-type housing, a flexible circuit board, a filter, and a photoelectric sensing component, is used to perform image acquisition on the scene in front of the camera lens to obtain and output a corresponding real-time acquisition image; A content learning mechanism, used for training the BP neural network for each time to obtain the BP neural network after completing each training action, and outputting the BP neural network after completing each training action, wherein the number of times the BP neural network completes the training action is monotonically positively correlated with the number of pixel circuits in the photoelectric sensing component; A continuous mapping device is connected to the targeted camera mechanism and the content learning mechanism, respectively, and is used to collect multiple camera parameters of the targeted camera mechanism and multiple image information of the real-time collected images, and based on the BP neural network after completing each training action, intelligently identifies whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images; A wireless transmission device, connected to the continuous mapping device, is used to intelligently identify that there is no image processing component inside the targeted camera mechanism, and wirelessly transmit the image identification of the instant captured image marked as a RAW image to a remote big data server; Wherein, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected images, and intelligently identifying whether there is an image processing component inside the targeted camera mechanism based on the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images based on the BP neural network after completing each training action includes: taking the ISO value, exposure time and dynamic range of grayscale value of the targeted camera mechanism as the multiple camera parameters of the targeted camera mechanism; Among them, collecting multiple camera parameters of the targeted camera mechanism and collecting multiple image information of the real-time collected images, and based on the BP neural network after completing each training action, intelligently identifying whether there is an image processing component inside the targeted camera mechanism according to the multiple camera parameters of the targeted camera mechanism and the multiple image information of the real-time collected images also includes: taking the number of main objects, foreground area ratio and maximum target area ratio of the real-time collected images as multiple image information of the real-time collected images.
2. The identification analysis system using image processing as claimed in claim 1, characterized in that: The wireless transmission device is also used to intelligently identify the existence of an image processing component inside the targeted camera mechanism, and wirelessly transmit the image identifier of the real-time captured image as a non-RAW image to a remote big data server.
3. The identification analysis system using image processing as claimed in claim 2, characterized in that: The system further comprises: An on-site storage device, connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, respectively, for providing data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner; Among them, the on-site storage device is respectively connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, and is used to provide data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner, including: the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device are respectively connected to the on-site storage device using different data channels.
4. The identification analysis system using image processing as claimed in claim 3, characterized in that: The on-site storage device is respectively connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, and is used to provide data temporary storage services for the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device in a time-sharing manner, including: the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device use the same reference clock signal.
5. The identification analysis system using image processing as claimed in claim 2, characterized in that: The system further comprises: The content display device is respectively connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device.
6. The identification analysis system using image processing as claimed in claim 5, characterized in that: The content display device is respectively connected to the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device, including: the various status information of the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device displayed simultaneously includes the sleep state or working state of the continuous mapping device, the targeted video recording mechanism, the content learning mechanism and the wireless transmission device.
7. The identification analysis system using image processing as claimed in claim 6, characterized in that: A content display device is respectively connected to the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted video camera mechanism, the content learning mechanism and the wireless transmission device, and also includes: the content display device is a liquid crystal display screen or an LED display array.
8. The identification analysis system using image processing as claimed in claim 7, characterized in that: The content display device is respectively connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and also includes: the LED display array is composed of multiple LED display units arranged in a rectangular matrix pattern.
9. The identification analysis system using image processing as claimed in claim 8, characterized in that: The content display device is respectively connected to the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and is used to simultaneously display various status information of the continuous mapping device, the targeted camera mechanism, the content learning mechanism and the wireless transmission device, and also includes: the structure of the multiple LED display units arranged in a rectangular matrix pattern is the same.