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Engineering machinery image recognition method and system fusing artificial fish and particle swarm optimization

A technology of artificial fish swarm algorithm and particle swarm algorithm, which is applied in computer parts, neural learning methods, character and pattern recognition, etc., can solve the problem of falling into local optimal values, affecting the accuracy of construction machinery image recognition, noise points and abnormal points Sensitivity and other issues to achieve the effect of improving accuracy

Pending Publication Date: 2022-01-07
SHANDONG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

The inventor found that in the traditional target detection algorithm, the selection of the initial anchor frame generally adopts the K-means clustering method, but this method is easy to fall into the local optimal value, and is sensitive to noise points and abnormal points, which in turn affects the image of construction machinery recognition accuracy

Method used

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  • Engineering machinery image recognition method and system fusing artificial fish and particle swarm optimization
  • Engineering machinery image recognition method and system fusing artificial fish and particle swarm optimization
  • Engineering machinery image recognition method and system fusing artificial fish and particle swarm optimization

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Embodiment 1

[0032] Such as figure 2 As shown, this embodiment provides a construction machinery image recognition method that combines artificial fish and particle swarm algorithm, which specifically includes the following steps:

[0033] S201: Acquire an image of a construction machine and initialize an anchor frame of the image of the construction machine.

[0034] In a specific implementation, the construction machinery images are images of trucks, excavators, road rollers, bulldozers, and the like.

[0035] Wherein, the anchor frame is a rectangular frame. In this embodiment, the category and position of the standard frame are marked with a rectangular frame.

[0036] S202: Use the fused particle swarm optimization algorithm and artificial fish swarm algorithm to process the initial anchor frame of the construction machinery image to obtain the standard anchor frame of the construction machinery image; wherein, each particle of the particle swarm optimization algorithm is regarded ...

Embodiment 2

[0066] Such as image 3 As shown, this embodiment provides a construction machinery image recognition system that combines artificial fish and particle swarm algorithm, which includes:

[0067] An image acquisition and initialization module 301, which is used to acquire the construction machinery image and initialize the anchor frame of the construction machinery image;

[0068] A standard anchor frame acquisition module 302, which is used to process the initial anchor frame of the construction machinery image by using the fused particle swarm optimization algorithm and artificial fish swarm algorithm to obtain the standard anchor frame of the construction machinery image; wherein, the particle swarm optimization algorithm Each particle is regarded as an artificial fish, the speed of each particle is regarded as the field of view of the artificial fish, and the field of view of the foraging behavior of the artificial fish swarm algorithm is adaptively changed through fitness o...

Embodiment 3

[0072] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the fusion of artificial fish and Steps in the particle swarm algorithm-based image recognition method for construction machinery.

[0073] refer to Figure 4 , a schematic structural diagram of the electronic device in this embodiment. It should be noted, Figure 4 The illustrated electronic device 400 is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present invention.

[0074] Such as Figure 4 As shown, the electronic device 400 includes a central processing unit (CPU) 401 which can execute various appropriate action and handling. In RAM 403, various programs and data necessary for system operation are also stored. The central processing unit 401 , the ROM 402 and the RAM 503 are connected to each...

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Abstract

The invention belongs to the field of engineering machinery image recognition, and provides an engineering machinery image recognition method and system fusing artificial fish and a particle swarm algorithm. The method comprises the following steps: acquiring an engineering machinery image and initializing an anchor frame of the engineering machinery image; processing the initial anchor frame of the engineering machinery image by using a fused particle swarm algorithm and an artificial fish swarm algorithm to obtain a standard anchor frame of the engineering machinery image; wherein each particle of the particle swarm algorithm is regarded as an artificial fish, the speed of each particle is regarded as the field of view of the artificial fish, the field of view of the foraging behavior of the artificial fish swarm algorithm is adaptively changed through fitness optimization, and the field of view is an anchor frame; and identifying the type of the engineering machinery in the engineering machinery image based on the standard anchor frame and the target detection model of the engineering machinery image.

Description

technical field [0001] The invention belongs to the field of image recognition of construction machinery, in particular to a method and system for image recognition of construction machinery integrating artificial fish and particle swarm algorithm. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] In recent years, with the surge of new technologies of artificial intelligence, the civil engineering industry has ushered in new opportunities and challenges. The traditional engineering management system has been unable to achieve the goal of intelligent, efficient, and closed-loop management. Researchers have begun to try to introduce new technologies to improve the real-time and accuracy of construction management under complex conditions. Vision sensors have become one of the indispensable sensors for construction machinery due to their low c...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/00G06N3/04G06N3/08
CPCG06N3/006G06N3/084G06N3/045G06F18/23213G06F18/25G06F18/241G06F18/214
Inventor 闫伟曲春燕纪嘉树胡滨侯衍华袁子洋
Owner SHANDONG UNIV
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