A method for operating a power device depth image generation system

Abstract

The application discloses a kind of operation methods of power equipment depth image generation system, it is related to the field for generating power equipment image with depth information.It includes total station and monocular camera, monocular camera is installed in the top of total station, and with total station collimation axis coaxial;Including the following steps: step 1, form RGB image;Step 2, screen effective pixels;Step 3, calculate observation element;Step 4, measure three-dimensional coordinates;Step 5, calculate depth information;Step 6, generate depth image.The application filters effective pixel points in RGB image by depth learning, and calculates the geometric observation element of total station to each effective pixel point by the relative position relationship between the photographic center of monocular camera and the geometric center of total station, realizes long-distance, high-precision distance measurement.

Description

Technical Field

[0001] This invention relates to the field of generating images of power equipment with depth information, and more specifically, to a method for operating a power equipment depth image generation system. Background Technology

[0002] Depth imaging uses the distance from each point in the image scene to the camera as a pixel value, and has been widely used in fields such as computer vision and photogrammetry.

[0003] Currently, depth images are mainly obtained through depth cameras, which operate on three principles: time-of-flight, binocular stereo vision, and structured light; all of these have certain limitations.

[0004] 1) Time-of-flight cameras generally have a small field of view and an operating distance of no more than 10m, and generate a lot of heat in a short time;

[0005] 2) Binocular stereo vision is limited by the baseline length, and can only guarantee good ranging results at close range;

[0006] 3) Structured light cameras have a short operating distance and are greatly affected by object reflections.

[0007] In summary, existing depth imaging systems all suffer from drawbacks such as short operating distance and low accuracy, making them unsuitable for power equipment inspection. This can easily lead to various damage risks due to the inspection equipment being too close to the power equipment.

[0008] Therefore, it is necessary to develop a method for using a power equipment depth image generation system to improve the limitations of existing depth images, which have a short range and are not applicable to power equipment. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the above-mentioned background technology and to provide an operation method for a power equipment depth image generation system.

[0010] To achieve the above objectives, the technical solution of the present invention is: an operation method for a power equipment depth image generation system, characterized in that: it includes a total station and a monocular camera, wherein the monocular camera is installed on the top of the total station and is coaxial with the line of sight of the total station;

[0011] Includes the following steps:

[0012] Step 1, Create an RGB image:

[0013] A monocular camera is used to photograph electrical equipment, creating RGB images;

[0014] Step 2, filter valid pixels:

[0015] Using deep learning algorithms, electrical equipment in RGB images is automatically identified, and the corresponding pixels of the electrical equipment are marked as valid pixels.

[0016] Step 3, calculate the observed elements:

[0017] Based on the relative positional relationship between the camera center and the total station's geometric center, the observation elements of each effective pixel relative to the total station's geometric center are calculated, namely, the horizontal angle and the vertical angle.

[0018] Step 4, Measure the three-dimensional coordinates:

[0019] Based on the calculated observation elements of each effective pixel, the total station uses a non-cooperative target method to measure each effective pixel and measure the three-dimensional spatial coordinates of the power equipment point corresponding to each effective pixel.

[0020] Step 5, calculate depth information:

[0021] Based on the three-dimensional spatial coordinates of the camera center and each effective pixel, the spatial distance between them is calculated, which is the depth information of each effective pixel.

[0022] Step 6, generate depth image:

[0023] The depth information of each valid pixel is used as the pixel value to generate a depth image.

[0024] In the above technical solution, the total station is an intelligent total station.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] 1) This invention uses deep learning to filter effective pixels in RGB images and calculates the geometric observation elements of the total station for each effective pixel by using the relative positional relationship between the camera center of the monocular camera and the geometric center of the total station, thus achieving long-distance, high-precision distance measurement.

[0027] 2) This invention uses deep learning to identify power equipment, thereby filtering out effective pixels in RGB images, which greatly improves the observation efficiency of the total station.

[0028] 3) This invention calculates the geometric observation elements of each effective pixel point by the total station based on the relative positional relationship between the imaging center of the monocular camera and the geometric center of the total station, enabling the total station to quickly perform three-dimensional coordinate measurements on power equipment. The total station adopts a non-cooperative target method, avoiding the placement of prisms or reflectors on the power equipment, while significantly increasing the measurement distance compared to existing technologies, reaching hundreds of meters or more. By using the coordinates of the imaging center of the monocular camera and the three-dimensional spatial coordinates of the effective pixels measured by the total station, the depth information of each effective pixel point is calculated, with a measurement accuracy down to the millimeter level. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of the power equipment depth image generation system of the present invention.

[0030] Figure 2 This is a flowchart of the operating method of the present invention.

[0031] Among them, 1-monocular camera, 2-total station, 3-power equipment. Detailed Implementation

[0032] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but these descriptions do not constitute a limitation of the present invention and are merely illustrative. The advantages of the present invention will become clearer and easier to understand through this description.

[0033] Referring to the attached drawings, a method for operating a depth image generation system for power equipment includes a total station 1 and a monocular camera 2, wherein the monocular camera 2 is mounted on the top of the total station 1 and is coaxial with the line of sight of the total station 1;

[0034] Includes the following steps:

[0035] Step 1, Create an RGB image:

[0036] The power equipment 3 is photographed using a monocular camera 2 to form an RGB image;

[0037] Step 2, filter valid pixels:

[0038] The deep learning algorithm is used to automatically identify the power device 3 in the RGB image and mark the corresponding pixels of the power device 3 as valid pixels;

[0039] Step 3, calculate the observed elements:

[0040] Based on the relative positional relationship between the imaging center of the monocular camera 2 and the geometric center of the total station 1, the observation elements of each effective pixel point relative to the geometric center of the total station 1 are calculated, namely the horizontal angle and the vertical angle.

[0041] Step 4, Measure the three-dimensional coordinates:

[0042] Based on the calculated observation elements of each effective pixel, the total station 1 uses the non-cooperative target method to measure each effective pixel and measure the three-dimensional spatial coordinates of the power equipment 3 corresponding to each effective pixel.

[0043] Step 5, calculate depth information:

[0044] Based on the three-dimensional spatial coordinates of the camera center of the monocular camera 2 and each effective pixel, the spatial distance between them is calculated, which is the depth information of each effective pixel.

[0045] Step 6, generate depth image:

[0046] The depth information of each valid pixel is used as the pixel value to generate a depth image.

[0047] The total station 1 is an intelligent total station.

[0048] This invention uses deep learning to identify power equipment, thereby filtering out effective pixels in RGB images, significantly improving the observation efficiency of the total station 1. By calculating the relative positional relationship between the center of the monocular camera 2 and the geometric center of the total station 1, the geometric observation elements of each effective pixel are deduced, enabling the total station 1 to quickly perform three-dimensional coordinate measurements on the power equipment 3. The total station 1 employs a non-cooperative target method, avoiding the need to place prisms or reflectors on the power equipment 3, while significantly increasing the measurement distance compared to existing technologies, reaching hundreds of meters or more. By using the coordinates of the center of the monocular camera 2 and the three-dimensional spatial coordinates of the effective pixels measured by the total station 1, the depth information of each effective pixel is calculated, achieving a measurement accuracy down to the millimeter level.

[0049] All other unspecified parts belong to the prior art.

Claims

1. A method for operating a power equipment depth image generation system, characterized in that: It includes a total station (1) and a monocular camera (2), wherein the monocular camera (2) is mounted on the top of the total station (1) and is coaxial with the line of sight of the total station (1); Includes the following steps: Step 1, Create an RGB image: A monocular camera (2) is used to take pictures of the power equipment (3) to form an RGB image; Step 2, filter valid pixels: The power equipment (3) in the RGB image is automatically identified using a deep learning algorithm, and the corresponding pixels of the power equipment (3) are marked as valid pixels; Step 3, calculate the observed elements: Based on the relative positional relationship between the camera center of the monocular camera (2) and the geometric center of the total station (1), the observation elements of each effective pixel point relative to the geometric center of the total station (1), namely the horizontal angle and the vertical angle, are calculated. Step 4, Measure the three-dimensional coordinates: Based on the calculated observation elements of each effective pixel, the total station (1) uses the non-cooperative target method to measure each effective pixel and measure the three-dimensional spatial coordinates of the power equipment (3) points corresponding to each effective pixel. Step 5, calculate depth information: Based on the three-dimensional spatial coordinates of the camera center of the monocular camera (2) and each effective pixel, the spatial distance between them is calculated, which is the depth information of each effective pixel. Step 6, generate depth image: The depth information of each valid pixel is used as the pixel value to generate a depth image; The method can measure distances of hundreds of meters or more with an accuracy down to the millimeter level. The total station (1) is an intelligent total station.

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