CN109285151A - A kind of AI intelligent dimming method - Google Patents

A kind of AI intelligent dimming method Download PDF

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Publication number
CN109285151A
CN109285151A CN201811109672.0A CN201811109672A CN109285151A CN 109285151 A CN109285151 A CN 109285151A CN 201811109672 A CN201811109672 A CN 201811109672A CN 109285151 A CN109285151 A CN 109285151A
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value
image
roi
region
intelligent dimming
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CN201811109672.0A
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CN109285151B (en
Inventor
韦受宁
周峰
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Dongguan Pomeas Precision Instrument Co ltd
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Dongguan Pomeas Precision Instrument Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B45/00—Circuit arrangements for operating light-emitting diodes [LED]
    • H05B45/10—Controlling the intensity of the light
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

Abstract

The present invention relates to intelligent dimming technical fields, refer in particular to AI intelligent dimming method, comprising the following steps: step 1: pre-processing to image, are effectively filtered for since actual environment influences generation picture noise；Step 2: treating polishing object by using deep learning and machine vision mode and carry out automatic identification, obtain polishing region of interest ROI；Step 3: to carrying out feature extraction to polishing object edge and texture in ROI and statistically analyze, in conjunction with human eye characteristic, the mathematical model for establishing a non-reference picture quality evaluation, by carrying out objective evaluation to picture quality, whether checking image quality is clear, uniform；Step 4: by local dimming, obtaining best GiLight value.The present invention automatically analyzes region of interest ROI using deep learning and machine vision method, realizes that the analysis of ROI region image intelligent is AI image quality measure, obtains the best G in each regioniLight value, to control each channel lamplight brightness.

Description

A kind of AI intelligent dimming method
Technical field
The present invention relates to intelligent dimming technical fields, refer in particular to a kind of AI intelligent dimming method.
Background technique
Industrial products are many kinds of, in the production process of industrial products, need to carry out observation detection to product, that is, need Obtain the image information of product.And the principal element for influencing image definition, details performance and gray-level performance is polishing.And Currently used polishing mode is manually to get to light source by polishing object, but such mode is by artificial master Viewing is rung greatly and otherness is larger, and the quality of polishing cannot be guaranteed unification, inefficiency, therefore, using a kind of automatical and efficient Polishing mode be people's urgent problem to be solved.
Summary of the invention
The present invention provides a kind of AI intelligent dimming method for the above technical issues.
In order to solve the above-mentioned technical problem, the present invention adopts the following technical scheme:
A kind of AI intelligent dimming method, comprising the following steps:
Step 1: image being pre-processed, carries out effective mistake for since actual environment influences the picture noise generated Filter；
Step 2: treating polishing object by using the mode of deep learning and machine vision and carry out automatic identification, obtain and beat The region of interest ROI of light；
Step 3: feature extraction being carried out to the edge and texture to polishing object in ROI and is statisticallyd analyze, in conjunction with human eye Distinctive feature establishes the mathematical model of a non-reference picture quality evaluation, by carrying out objective evaluation to picture quality, examines Whether picture quality is clear, uniform；
Step 4: by local dimming, obtaining best GiLight value.
Further, step 3 specifically includes: objective evaluation is carried out to picture quality by following formula:
Wherein roiMSE indicates the mean square error of image gradient, and W, H are the width and height to polishing object in ROI region, GmeanFor the average gray value on present image gradient locations (i, j), WijFor the gray value of (i, j) in sliding window；roiPSNR For Y-PSNR, the i.e. ratio of peak signal amount L and noise intensity；RoiDIS indicates the line in ROI region to polishing object Discrete feature is managed, wherein Bi、Bmean、BmaxFor current value, mean value, the maximum for carrying out gray-scale watermark statistics in unit window Value.
Then following image quality measure function is proposed:
Score=W1×roiPSNR+W2×mean(Sobel(img))+W3×roiDIS；
Wherein W1、W2And W3It is particular value, passes through certain amount sample analysis and the warp obtained in conjunction with human eye distinctive feature Value is tested, G is found outmax。
Further, step 4 specifically includes:
Step 4.1: by global light, calculating max (Score) and obtain the best gradient of image, and obtain corresponding GmaxAnd roiPSNRg,roiDISg；
Step 4.2: area maps being carried out according to LED light source characteristic, roiPSNRi, the roiDISi for calculating each region are obtained G outi；
Step 4.3: controlling each channel G respectivelyiValue, is intended to Gmax, step 4.2 and step 4.3 are iterated to calculate, is calculated Loss1=fabs (roiPSNRi-roiPSNRg), Loss2=fabs (roiDISi-roiDISg) out；
Step 4.4: solving min (Loss1+Loss2), acquire the best G in each channeliLight value.
Further, step 1 specifically includes: RGB channel separates gaussian filtering and connected domain filtering.
Further, horizontally and vertically for the region of interest ROI of polishing, it is calculated by gradient analysis Gradient both horizontally and vertically, to detect gaussian filtering and whether the filtered image of connected domain is clear.
Beneficial effects of the present invention: the present invention is without setting reference picture, using the method for deep learning and machine vision Region of interest ROI is automatically analyzed, realizes that intelligence is AI image quality measure, obtains the best G in each regioniLight value, from And control the luminosity that each channel adjusts corresponding light.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention.
Specific embodiment
For the ease of the understanding of those skilled in the art, the present invention is made further below with reference to embodiment and attached drawing Bright, the content that embodiment refers to not is limitation of the invention.The present invention is described in detail below in conjunction with attached drawing.
Refering to Figure 1, a kind of AI intelligent dimming method provided by the invention, comprising the following steps:
Step 1: image being pre-processed, carries out effective mistake for since actual environment influences the picture noise generated Filter；
Step 2: treating polishing object by using the mode of deep learning and machine vision and carry out automatic identification, obtain and beat The region of interest ROI of light；
Step 3: feature extraction being carried out to the edge and texture to polishing object in ROI and is statisticallyd analyze, in conjunction with human eye Distinctive feature establishes the mathematical model of a non-reference picture quality evaluation, by carrying out objective evaluation to picture quality, examines Whether picture quality is clear, uniform；
Step 4: by local dimming, obtaining best GiValue.
The present invention automatically analyzes area-of-interest using the method for deep learning and machine vision without setting reference picture ROI realizes that intelligence is AI image quality measure, obtains the best G in each regioniLight value, to control each channel adjustment The luminosity of corresponding light.
Specific automatic identification mode includes carrying out image object point using the method for machine vision for common object It cuts, object detection is carried out using deep learning method for some specific objects, specific deep learning method includes but not It is defined in the neural networks such as VGG, resNet.
A kind of AI intelligent dimming method, step 3 described in the present embodiment specifically include:
Objective evaluation is carried out to picture quality by following formula:
Wherein roiMSE indicates the mean square error of image gradient, and W, H are the width and height to polishing object in ROI region, GmeanFor the average gray value on present image gradient locations (i, j), WijFor the gray value of (i, j) in sliding window；roiPSNR For Y-PSNR, the i.e. ratio of peak signal amount L and noise intensity；RoiDIS indicates the line in ROI region to polishing object Discrete feature is managed, wherein Bi、Bmean、BmaxFor current value, mean value, the maximum for carrying out gray-scale watermark statistics in unit window Value.
Then following image quality measure function is proposed:
Score=W1×roiPSNR+W2×mean(Sobel(img))+W3×roiDIS；
Wherein W1、W2And W3It is particular value, passes through certain amount sample analysis and the warp obtained in conjunction with human eye distinctive feature Value is tested, G is found outmax.Specifically, wherein W1It is 0.62, W2It is 0.24, W3It is 0.14.
Wherein, the MSE is mean squared error, and the PSNR is that peak signal to noise, AI are Artificial intelligence Artificial Intelligence, GmaxFor the maximum value in calculated G value, G value is light value.
The sliding window is 3 × 3 neighborhoods, and peak signal amount is single channel image maximum gradation value L=255.
A kind of AI intelligent dimming method, step 4 described in the present embodiment specifically include:
Step 4.1: by global light, calculating max (Score) and obtain the best gradient of image, and obtain corresponding GmaxAnd roiPSNRg,roiDISg；
Step 4.2: area maps being carried out according to LED light source characteristic, roiPSNRi, the roiDISi for calculating each region are obtained G outi；
Step 4.3: controlling each channel G respectivelyiValue, is intended to Gmax, step 4.2 and step 4.3 are iterated to calculate, is calculated Loss1=fabs (roiPSNRi-roiPSNRg), Loss2=fabs (roiDISi-roiDISg) out；
Step 4.4: solving min (Loss1+Loss2), acquire the best G in each channeliLight value.
Wherein roiPSNRi and GiIn i indicate ith zone, GiValue refers to the G value in ith zone, roiPSNRi For the Y-PSNR of ith zone.
A kind of AI intelligent dimming method, step 1 described in the present embodiment specifically include: RGB channel separate gaussian filtering and Connected domain filtering.
Specifically, the gaussian filtering is referred to an each of template (or convolution, mask) scan image Pixel goes the value of alternate template central pixel point with the weighted average gray value of pixel in the determining neighborhood of template, for effective The picture noise that ground filtering is introduced due to illumination, light modulator, camera.
Specifically, the picture noise that connected domain filtering effectively filtering is introduced due to illumination, light modulator, camera.
A kind of AI intelligent dimming method described in the present embodiment, for polishing region of interest ROI horizontal direction and Vertical direction calculates gradient both horizontally and vertically by gradient analysis, that is, Sobel method, to detect gaussian filtering and company Whether the logical filtered image in domain is clear.
The above is only present pre-ferred embodiments, is not intended to limit the present invention in any form, although The present invention is disclosed as above with preferred embodiment, and however, it is not intended to limit the invention, any person skilled in the art, It does not depart within the scope of technical solution of the present invention, when the technology contents using the disclosure above make a little change or are modified to equivalent change The equivalent embodiment of change, but without departing from the technical solutions of the present invention, technology refers to above embodiments according to the present invention Made any simple modification, equivalent change and modification, belong in the range of technical solution of the present invention.

Claims (5)

1. a kind of AI intelligent dimming method, it is characterised in that: the following steps are included:
Step 1: image being pre-processed, is effectively filtered for the picture noise for influencing to generate due to actual environment；
Step 2: treating polishing object by using the mode of deep learning and machine vision and carry out automatic identification, obtain polishing Region of interest ROI；
Step 3: feature extraction being carried out to the edge and texture to polishing object in ROI and is statisticallyd analyze, is distinguished in conjunction with human eye Characteristic establishes the mathematical model of a non-reference picture quality evaluation, by carrying out objective evaluation, checking image to picture quality Whether quality is clear, uniform；
Step 4: by local dimming, obtaining best GiLight value.
2. a kind of AI intelligent dimming method according to claim 1, it is characterised in that:
Step 3 specifically includes: objective evaluation is carried out to picture quality by following formula:
Wherein roiMSE indicates the mean square error of image gradient, and W, H are the width and height to polishing object in ROI region, GmeanFor Average gray value on present image gradient locations (i, j), WijFor the gray value of (i, j) in sliding window；RoiPSNR is peak value Signal-to-noise ratio, the i.e. ratio of peak signal amount L and noise intensity；RoiDIS indicates that the texture in ROI region to polishing object is discrete Characteristic, wherein Bi、Bmean、BmaxFor current value, mean value, the maximum value for carrying out gray-scale watermark statistics in unit window.
Then following image quality measure function is proposed:
Score=W1×roiPSNR+W2×mean(Sobel(img))+W3×roiDIS；
Wherein W1、W2And W3It is particular value, passes through certain amount sample analysis and the experience obtained in conjunction with human eye distinctive feature Value, finds out Gmax。
3. a kind of AI intelligent dimming method according to claim 2, it is characterised in that:
Step 4 specifically includes:
Step 4.1: by global light, calculating max (Score) and obtain the best gradient of image, and obtain corresponding GmaxAnd roiPSNRg,roiDISg；
Step 4.2: area maps being carried out according to LED light source characteristic, roiPSNRi, the roiDISi for calculating each region obtain Gi；
Step 4.3: controlling each channel G respectivelyiValue, is intended to Gmax, step 4.2 and step 4.3 are iterated to calculate, is calculated Loss1=fabs (roiPSNRi-roiPSNRg), Loss2=fabs (roiDISi-roiDISg)；
Step 4.4: solving min (Loss1+Loss2), acquire the best G in each channeliLight value.
4. a kind of AI intelligent dimming method according to claim 1, it is characterised in that: step 1 specifically includes: RGB channel Separate gaussian filtering and connected domain filtering.
5. a kind of AI intelligent dimming method according to claim 4, it is characterised in that: for the area-of-interest of polishing ROI horizontally and vertically, by gradient analysis calculates gradient both horizontally and vertically, to detect gaussian filtering It is whether clear with the filtered image of connected domain.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111707455A (en) * 2020-07-03 2020-09-25 深圳爱克莱特科技股份有限公司 Smooth dimming method and system for lamp
CN113189113A (en) * 2021-04-30 2021-07-30 聚时科技(上海)有限公司 Intelligent digital light source and method based on visual detection

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102466945A (en) * 2010-11-19 2012-05-23 北京海鑫智圣技术有限公司 LED supplementary lighting and image clipping evaluation system in standard image acquisition device
US20180068195A1 (en) * 2016-09-07 2018-03-08 Apple, Inc. Multi-Dimensional Objective Metric Concentering
CN107959767A (en) * 2017-12-14 2018-04-24 中国科学院长春光学精密机械与物理研究所 A kind of focusing light-dimming method using TV track result as guiding

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102466945A (en) * 2010-11-19 2012-05-23 北京海鑫智圣技术有限公司 LED supplementary lighting and image clipping evaluation system in standard image acquisition device
US20180068195A1 (en) * 2016-09-07 2018-03-08 Apple, Inc. Multi-Dimensional Objective Metric Concentering
CN107959767A (en) * 2017-12-14 2018-04-24 中国科学院长春光学精密机械与物理研究所 A kind of focusing light-dimming method using TV track result as guiding

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111707455A (en) * 2020-07-03 2020-09-25 深圳爱克莱特科技股份有限公司 Smooth dimming method and system for lamp
CN111707455B (en) * 2020-07-03 2021-08-13 深圳爱克莱特科技股份有限公司 Smooth dimming method and system for lamp
CN113189113A (en) * 2021-04-30 2021-07-30 聚时科技(上海)有限公司 Intelligent digital light source and method based on visual detection

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