Bridge deformation monitoring method and device based on visual perception, and equipment
A visual perception, bridge technology, applied in the field of image processing, can solve problems such as failure, high cost, limited precision, etc., to achieve the effect of rich feature information, fast monitoring speed, and speed up
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Embodiment 1
[0027] figure 1 It is a flowchart of a method for monitoring bridge displacement based on visual perception provided by the first embodiment of the present invention. This embodiment is applicable to the monitoring of bridge displacement. The method can be executed by a bridge displacement monitoring device or system. The device can be implemented by software and hardware, such as figure 1 As shown, the method specifically includes the following steps:
[0028] Step 110: Obtain at least two initial images collected by the binocular camera.
[0029] Wherein, the at least two frames of initial images may be at least two consecutive initial images, and may be at least two frames of initial images at a specified interval. It can be two frames, three frames, four frames or more. Binocular camera refers to a camera that includes two lenses. Specifically, the binocular camera is an industrial camera including two lenses.
[0030] Optionally, the acquiring at least two initial images coll...
Embodiment 2
[0050] figure 2 This is a flowchart of a method for monitoring bridge displacement based on visual perception provided in the second embodiment of the present invention. This embodiment is a further refinement of the previous embodiment, such as figure 2 As shown, the method includes the following steps:
[0051] Step 210: Obtain at least two initial images collected by the binocular camera.
[0052] Step 220: Convert the at least two frames of initial images into at least two frames of integral images.
[0053] Step 230: Filter the at least two frames of integral images according to the box filter.
[0054] Step 240: Construct a filtered Hessian matrix of the at least two frames of integral images.
[0055] Step 250: Identify feature points of the at least two frames of integral images according to the discriminant of the Hessian matrix.
[0056] Specifically, the function of the integral image is I(x, y), and the box filter is represented by g(σ), where σ is a smoothness parameter, a...
Embodiment 3
[0090] image 3 This is a flowchart of a method for monitoring bridge displacement based on visual perception provided in the third embodiment of the present invention. This embodiment is a further refinement and supplement to the previous embodiment. The method provided in this embodiment further includes: The binocular correction algorithm of the binocular camera eliminates the distortion of the left initial image and the right initial image of the at least two frames of initial images; according to the polar constraint characteristics of the binocular camera, the left initial image and the right initial image Aligning in rows, so that the epipolar lines of the left initial image and the right initial image are on the same horizontal line; and according to the epipolar constraint characteristics of the binocular camera, the matched feature points that are not in the same row are eliminated.
[0091] Such as image 3 As shown, the method for monitoring bridge displacement provide...
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