葛仁彥-機(jī)器視覺的自適應(yīng)算法在工業(yè)自動化檢測中的應(yīng)用

AutomaticSLInspectionEquipment1Nanovision?Contents目錄1Background背景資料2AdaptiveAlgorithmIntroduction自適應(yīng)算法簡介3AdaptiveFilter自適應(yīng)濾波4AdaptivePatternRestoration自適應(yīng)模式恢復(fù)5AdaptivePatternMatching自適應(yīng)模式匹配6TypicalApplicationCase典型應(yīng)用案例7FutureWork將來的工作Nanovision?1.Background背景資料IndustrialvisionsystemsrequireexpertiseinmultiplefieldsastheyarecompositionsofdifferenttechnologiesArchitecturesSoftware?PC+CameraSensors?EmbeddedVision?Identification?SmartCameras?CMOS?Measurement?PC+SmartCamera?CCD?Guidance?CMOS/CCD?Other?OtherOptics?LensesMachineVisionDataCapture?Prisms&BeamSplittersRobiticGuidance?Analog?ColorFiltersFactoryAutomation?Digital?PolarizersProcessorsSpectrum/Illumination?FPGAInterfaces?Visible?LED(FieldProgrammableGatrArray)?GigabitEthernet(GigE)?DSP?Infrared(IR)?Halogen?Firewire(1394b)(DigitalSignalProcessor)?Ultraviolet(UV)?Other?CameraLink?RISC?USB2.0-3.0(ReconfigurableInstructionSetComputer)?Other?FrameGrabbersSource：Frost&Sullivan，2010Nanovision?Background背景資料CurrentAlgorithmsmethodforIndustrialParts/SurfaceInspection目前在工業(yè)部件和表面檢測中的常用算法CADBasedPatternMatching與CAD設(shè)計圖案匹配GoldenTemplateMatchingDesignRuleCheckDRC金色模板匹配設(shè)計規(guī)則檢查ArtificialNeuralNetworkFeaturebasedCompare人工神經(jīng)網(wǎng)路基于特征的比較Nanovision?Background背景資料目前算法存在的問題CADBasedPatternMatching（與CAD設(shè)計圖案匹配）CAD模板圖像化過程比較繁瑣CAD模板與實(shí)際采集圖像很難配準(zhǔn)，影像采集根據(jù)應(yīng)用不同有不同程度的變形GoldenTemplateMatching（金色模板匹配）需要很多良品制作金色模板，沒有統(tǒng)一性受影像采集過程中運(yùn)動等影響，模板匹配會有局部偏差DesignRuleCheck–DRC（設(shè)計規(guī)則檢查）DRC需要知道被檢測物品的設(shè)計規(guī)則，需要經(jīng)驗值DRC只能檢測非常微小的局部缺陷，有局限性ArtificialNeuralNetwork（人工神經(jīng)網(wǎng)絡(luò)）學(xué)習(xí)矩陣需要大量的樣本計算量比較大，計算收斂慢，對在線實(shí)時檢測需要FPGA或DSP的加速計算FeaturebasedCompare（基于特征的比較）特征點(diǎn)的提取計算量比較大被檢測物品的特征很難量化，有的物品特征不是很明顯Nanovision?AdaptiveAlgorithmIntroduction自適應(yīng)算法簡介AdaptiveBackgroundnormalization自適應(yīng)背景歸一化AdaptiveFilter自適應(yīng)濾波

AdaptiveAlgorithmsMachineVision

AdaptivePatternRestoration自適應(yīng)模式恢復(fù)AdaptivePatternMatching自適應(yīng)模式匹配Nanovision?AdaptiveFilter自適應(yīng)濾波自適應(yīng)濾波存在于信號處理、控制、圖像處理等許多不同領(lǐng)域，它是一種智能更有針對性的濾波方法，通常用于去噪。自適應(yīng)濾波是近年以來發(fā)展起來的一種最佳濾波方法。它是在維納濾波（wienerfiltering),Kalman濾波等線性濾波基礎(chǔ)上發(fā)展起來的一種最佳濾波方法。由于它具有更強(qiáng)的適應(yīng)性和更優(yōu)的濾波性能。算法包括:LMS自適應(yīng)濾波算法RLS自適應(yīng)濾波算法變換域自適應(yīng)濾波算法仿射投影算法共扼梯度算法基于子帶分解的自適應(yīng)濾波算法基于QR分解的自適應(yīng)濾波算法在圖像處理中的應(yīng)用它是指在一張圖像的不同區(qū)域具有各自的圖像特性時，分別針對這些不同特性選取最優(yōu)的、各不相同的參數(shù)，濾波器或濾波方法進(jìn)行濾波。Nanovision?AdaptivePatternRestoration自適應(yīng)模式恢復(fù)自適應(yīng)模式恢復(fù)的主要技術(shù)要點(diǎn)：AutomaticNoiseRemove自動噪聲去除AutomaticBinarization（multi-levelsegmentation）自動二值化或多值分割FindtheregulationofthepatternbasedonFrequencydomainandSpacedomain根據(jù)頻率域和空間域的信息發(fā)現(xiàn)模式的規(guī)律性Patternrestorationbasedonregularfrequencyandtones根據(jù)影像的頻率和色調(diào)恢復(fù)重建具有規(guī)律的影像Nanovision?AdaptivePatternMatching自適應(yīng)模式匹配DigitalCameras

AdaptiveAdaptiveTemplateFilterBufferRegistrationActiveAdaptivePatternPipelineRegistrationRestorationObject

DynamicAdaptiveAutomaticDefectPatternDefectEnhancementMatchingClassificationHostClassifiedDefectsNanovision?TypicalApplicationCase典型應(yīng)用案例TypicalApplicationCase典型應(yīng)用案例：前端市場：AutomaticLCD/FTFInspectionSystem(LCD/TFTAOI)AutomaticScreenInspectionSystem(ScreenAOI)AutomaticshadowmaskInspectionSystem(ShadowMaskAOI)后端市場：導(dǎo)光板質(zhì)量檢測手機(jī)玻璃蓋板質(zhì)量檢測手機(jī)模組質(zhì)量檢測工業(yè)部件檢測：電阻應(yīng)變片檢測被動元器件材料檢測Nanovision?TypicalApplicationCase典型應(yīng)用案例AutomaticLCD/FTFInspectionSystem(LCD/TFTAOI)Nanovision?TypicalApplicationCase典型應(yīng)用案例AutomaticScreenInspectionSystem(ScreenAOI)Nanovision?TypicalApplicationCase典型應(yīng)用案例AutomaticshadowmaskInspectionSystem(ShadowMaskAOI)Nanovision?TypicalApplicationCase典型應(yīng)用案例導(dǎo)光板質(zhì)量檢測Nanovision?TypicalApplicationCase典型應(yīng)用案例手機(jī)玻璃蓋板質(zhì)量檢測：Nanovision?TypicalApplicationCase典型應(yīng)用案例手機(jī)模組質(zhì)量檢測（A）flexible 電路板ControlPWBSource信號GateGate信號PWBASICConnecter

SourcePWB間隔橡膠液晶面板 注入口封口處Nanovision?TypicalApplicationCase典型應(yīng)用案例手機(jī)模組質(zhì)量檢測（B）MuraInspectionisthekeyTwodimensionWaveletTransform,f(x,y)L2(R),cwt(s,a,b)1f(x,y)(xa,yb)dxdy(timedomain)sssCWT (s,w1,w2)sF(w1,w2)(sw1,sw2) (frequencydomain) Nanovision?TypicalApplicationCase典型應(yīng)用案例電阻應(yīng)變片檢測Nanovision?TypicalApplicationCase典型應(yīng)用案例被動元器件材料檢測斷線 缺損圖案缺損 網(wǎng)版劃傷Nanovision?FutureWork將來的工作自適應(yīng)算法將來要解決的主要問題：對規(guī)律性不強(qiáng)的影像處理影像檢測缺陷的自動分類問題現(xiàn)有算法在SmartCamera或FPGA/DSP上實(shí)現(xiàn)的可能性人工神經(jīng)網(wǎng)計算的快速收斂和加速問題人工智能（AI）在模式識別和工業(yè)自動化檢測中的應(yīng)用前景Nanovision?FutureWork將來的工作20092014201520202021&BeyondComponents

DynamicallyReconfigurableProcessorswilldisplaceFPGAsMulti-spectral(UVtoshortIR)sensorswillgainindustrialinterestandapplicationsAnaloguecamerasystemswillbereplacedwithdigitalcamerasTheneedforframegrabberswilldecreaseCMOSsensorswillgainadvantageoverCCDsduetolowerpowerconsumption,lowerpriceandhigherspeed

Old-to-newtechnologyreplacements(highersensorresolutionandcapturespeed)Embedded,industry-orientedsystemswillreplacePC-basedsystemsinthereplacementwaveMulti-spectralsensorswillgoclosertolongIRwavelengths(closeto8microns)andwillbelimitedbylensmaterialonmanufacturingInfluence

?Highaccuracymeasurementsin3Dandreal?Industrialvisionsystemswillbecometimewillbepossibleindispensablepartofflexibleautomationsystems?Machinevisionwillaidstatisticalanalysis,?Machinevisionsystemswillaidrapiddiagnosticsandmaintenanceinfactoriesmanufacturing?Remotecontrolandmonitoringofmachine?VisionsystemswillfindsafetyapplicationinvisionsystemswillbecommonfactoriesMachinevisionwillbeanintegralcontrolandguidancesolutionofrapidmanufacturingsystemsOtherRobotics

?Visionguidedrobotswilldecreasecycletime?Cycletimeofvisionguidedrobotsshallfall?Autonomousrobotswillusevisionsystemsbelow1secondenablingflexibleandfastrobotforhumanandobstacledetection,objectoperationtracking,navigationandlocalization?Visualservoingwillenablerobotstoaidflexible?Robotswillbereadytointelligentlypickprecision