Dirt detection method, device and equipment and storage medium

A detection method and dirty technology, which is applied in the field of image processing, can solve the problems of slow image detection speed and low detection accuracy, and achieve the effects of uniform gray scale, improved detection speed, and improved accuracy

Pending Publication Date: 2022-04-05
苏州凌云视界智能设备有限责任公司 +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method of processing only through gray threshold segmentation has low detection accuracy.
At the same time, this method is not suitable for the detection of large dirt on the surface of the product, and the detection speed is slow for images with large frames.

Method used

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  • Dirt detection method, device and equipment and storage medium
  • Dirt detection method, device and equipment and storage medium
  • Dirt detection method, device and equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0027] figure 1 It is a flowchart of a dirt detection method provided in Embodiment 1 of the present application. The embodiments of the present application may be applicable to detecting the contamination of the surface of the article, and the method may be implemented by a device for detecting contamination, which may be implemented by software and / or hardware, and specifically configured in an electronic device.

[0028] refer to figure 1 A dirty detection method shown specifically includes the following steps:

[0029] S110. Acquire an original image of the surface of the object to be detected.

[0030] Wherein, the surface of the object to be detected may be the surface of an object that requires dirt detection, such as a display screen, a painting, a handicraft, and the like. The original image can be an acquired image of the surface of the object to be detected, and the acquisition method can be photographed by a camera. Preferably, a high-resolution line scan camer...

Embodiment 2

[0048] figure 2It is a flowchart of a dirt detection method provided in Embodiment 2 of the present application. The embodiment of the present application refines the dirt detection operation in the detection area on the basis of the technical solutions of the foregoing embodiments, so as to improve the speed and accuracy of dirt detection.

[0049] refer to figure 2 A dirty detection method shown specifically includes the following steps:

[0050] S210. Acquire an original image of the surface of the object to be detected.

[0051] S220. Divide the original image into detection areas to obtain at least two detection areas.

[0052] S230. When concurrently performing dirt detection on at least two detection areas, for each detection area, perform pixel expansion on the detection area to obtain a corresponding extended detection area, and determine the dirt condition of the detection area corresponding to the extended detection area.

[0053] Wherein, the pixel expansion ...

Embodiment 3

[0072] Figure 3A It is a flowchart of a dirt detection method provided in Embodiment 3 of the present application. Preferably, on the basis of the foregoing implementation manners, this embodiment of the present application provides a preferred implementation manner by taking the detection of dirt on a liquid crystal screen of a display as an example.

[0073] Such as Figure 3A As shown, the dirt detection method may include: image acquisition, division of detection areas, expansion of detection areas, identification of expanded detection areas, elimination of interference from bright spots, image enhancement, screening of dirt, restoration of dirty images, and other steps.

[0074] Exemplarily, in image collection, in order to realize high-precision dirt detection, it is necessary to use a high-resolution line scan camera to collect images. The memory occupied by the collected grayscale images should not be less than 200M, and the liquid crystal area that needs to be dete...

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Abstract

The embodiment of the invention discloses a smudginess detection method and device, equipment and a storage medium. An original image of the surface of a to-be-detected object is taken; performing detection area division on the original image to obtain at least two detection areas; and carrying out parallel smudginess detection on the at least two detection areas to obtain the smudginess condition of the to-be-detected object. According to the technical scheme provided by the embodiment of the invention, the original image is subjected to detection area division, so that the problem that the original image is too large and the gray level distribution is not uniform can be effectively solved, the gray levels of the pixel points in each detection area tend to be uniform, the inaccurate detection result caused by large gray level difference of different areas in the original image is prevented, and the accuracy of the detection result is improved. Therefore, the smudginess detection precision is improved. Moreover, parallel detection is carried out on each detection area, so that the detection speed can be effectively improved.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular, to a dirt detection method, device, device, and storage medium. Background technique [0002] At present, before packaging physical products, they are prone to surface dirt during the production process, such as water stains or dust, etc., and the resulting dirt on the surface of the products will affect the sales of the products. Therefore, it is very important to detect the dirt on the surface of the product for the quality assurance of the product and to improve the efficiency of quality inspection. [0003] In the prior art, a threshold segmentation method is used to identify the dirt on the commodity surface. The pixel points in the image of the product surface are screened through the preset gray threshold to obtain the result of dirt recognition. However, the detection accuracy of this processing method only by gray threshold segmen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/136
Inventor 贾国靖周钟海姚毅杨艺
Owner 苏州凌云视界智能设备有限责任公司
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