本文選題：圖像畸變校正 + 多個自由平面標定法�。� 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：攝像機透鏡晶體材料折射率等固有屬性、透鏡表面制作工藝導(dǎo)致光滑程度參差不齊以及人工組裝等隨機因素,不可避免造成成像時的幾何畸變現(xiàn)象,影響了圖像信息的原始性,當該圖像用于后續(xù)處理時,不能得到對象的真實信息,因此研究畸變校正技術(shù)具有現(xiàn)實意義。SF6斷路器是電力系統(tǒng)中的關(guān)鍵設(shè)備之一,電力規(guī)程需要定時人工現(xiàn)場巡檢其氣體壓力、密度值,而壓力表盤位置過高或位于夾層都會使得工作人員難以直觀其指針示值,通過機器識別技術(shù)和遙視技術(shù)相結(jié)合,實現(xiàn)電力設(shè)備工作參數(shù)實時監(jiān)測也具有重要意義。論文針對因攝像機鏡頭成像造成的圖像幾何畸變問題,研究了基于粒子群算法與多個自由平面標定法融合的圖像畸變校正方法,并將其應(yīng)用于工業(yè)圖像機器識別中。分析了攝像機鏡頭畸變成因、畸變類型以及非線性畸變校正方法;采用多個自由平面標定法對攝像機數(shù)學(xué)模型參數(shù)進行標定;針對粒子群算法,給出了基于目標值的慣性權(quán)重因子計算方法;利用改進的粒子群算法優(yōu)化了攝像機數(shù)學(xué)模型的標定參數(shù),從而對非線性畸變圖像進行校正。最后,分別用迭代法和Otsu算法對校正后的SF6斷路器壓力表盤指針圖像進行閾值分割;并對比Sobel算子、Canny算子和形態(tài)學(xué)三種方法,對分割結(jié)果提取邊緣信息;利用基于Hough變換的直線與圓提取方法定位指針,從而將指針指示角度變換為壓力示值,實現(xiàn)了視感檢測在電力系統(tǒng)智能遙視中的應(yīng)用。實驗結(jié)果表明,給出的基于改進粒子群算法參數(shù)優(yōu)化的多個自由平面標定法的圖像畸變校正技術(shù)能夠較好復(fù)原畸變圖像,預(yù)處理的結(jié)果為后續(xù)實現(xiàn)分割和邊緣信息提取提供了保證。將視感檢測融入電力系統(tǒng)中的"四遙"技術(shù),豐富了遙視技術(shù)的內(nèi)涵,替代了人工定時巡檢,降低了遠程視頻監(jiān)控人員的目測工作強度。
[Abstract]:The intrinsic properties such as refractive index of camera lens crystal material, uneven smoothness of lens surface and random factors such as artificial assembly, etc., inevitably lead to geometric distortion in imaging, which affects the originality of image information.When the image is used for subsequent processing, the real information of the object can not be obtained. Therefore, it is of practical significance to study the distortion correction technology. SF6 circuit breaker is one of the key equipments in power system.The electric power regulations require regular manual on-site inspection of the gas pressure and density values, and the high position of the pressure gauge disc or its location in the interlayer will make it difficult for the staff to visualize the pointer value, which can be combined with machine recognition technology and remote viewing technology.It is also of great significance to realize real-time monitoring of working parameters of power equipment.In order to solve the problem of image geometric distortion caused by camera lens imaging, the image distortion correction method based on particle swarm optimization (PSO) and multi-free plane calibration method is studied and applied to industrial image machine recognition.The causes of camera lens distortion, the type of distortion and the correction method of nonlinear distortion are analyzed. The mathematical model parameters of camera are calibrated by using multiple free plane calibration methods, and the particle swarm optimization algorithm is used to calibrate the mathematical model parameters.The calculation method of inertial weight factor based on target value is given, and the calibration parameters of the mathematical model of camera are optimized by using improved particle swarm optimization algorithm to correct the nonlinear distorted image.Finally, we use iterative method and Otsu algorithm to segment the corrected SF6 circuit breaker pressure gauge pointer image, and compare the three methods of Sobel operator Canny operator and morphology to extract edge information from the segmentation results.The method of extracting straight line and circle based on Hough transform is used to locate the pointer, and then the pointer indication angle is transformed into the pressure indication value, and the application of visual sense detection in intelligent remote view of power system is realized.The experimental results show that the proposed image distortion correction technique based on the improved particle swarm optimization (PSO) algorithm with multiple free plane calibration methods can recover the distorted image well.The preprocessing results provide a guarantee for the subsequent implementation of segmentation and edge information extraction.The integration of visual sense detection into the "four remote" technology in power system enriches the connotation of remote viewing technology, replaces the manual timing inspection, and reduces the visual intensity of remote video surveillance personnel.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18

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