本文選題：絕緣子 + LBP　； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：接觸網(wǎng)及其附屬部件的良好工作狀態(tài)是高速列車(chē)安全運(yùn)行的基本保障。由于其長(zhǎng)時(shí)間處于工作狀態(tài),接觸網(wǎng)的絕緣子和定位支座不斷受到電氣沖擊及機(jī)械應(yīng)力的影響,如果絕緣子和定位支座發(fā)生故障,輕則損壞接觸網(wǎng)設(shè)備,重則將會(huì)直接導(dǎo)致高速列車(chē)的驟停甚至造成人員傷亡。目前,基于圖像處理技術(shù)的絕緣子工作狀態(tài)檢測(cè)已有研究,這大大提高了設(shè)備巡檢效率,節(jié)省了不少人力財(cái)力。然而,以往的檢測(cè)方法基本是針對(duì)絕緣子片缺失或夾雜異物的研究,絕緣子破裂故障的研究較少,且定位支座的檢測(cè)幾乎為空白,因此有必要采用圖像處理中機(jī)器學(xué)習(xí)的高效方法檢測(cè)絕緣子和定位支座。在對(duì)以往接觸網(wǎng)及附屬裝置檢測(cè)方法、電力系統(tǒng)中絕緣子的識(shí)別進(jìn)行研究以及對(duì)比人臉識(shí)別方法之后,本文利用接觸網(wǎng)綜合巡檢車(chē)采集的圖像數(shù)據(jù)作為樣本的原始圖像,利用LBP和HOG提取絕緣子的局部特征,接著采用機(jī)器學(xué)習(xí)的方法訓(xùn)練分類(lèi)器對(duì)圖像中的絕緣子進(jìn)行精確提取,然后對(duì)絕緣子裂紋進(jìn)行分析。同時(shí)采用基于ASIFT、SURF、ORB、FREAK四種特征匹配的方法實(shí)現(xiàn)了小目標(biāo)定位支座的檢測(cè)。首先對(duì)絕緣子的原始圖像做了形態(tài)學(xué)運(yùn)算等相關(guān)預(yù)處理,建立了以大量絕緣子目標(biāo)和非絕緣子圖像為基礎(chǔ)的正負(fù)樣本庫(kù),然后提取絕緣子的LBP和HOG特征。利用機(jī)器學(xué)習(xí)的方法提取圖像中的目標(biāo)絕緣子。分別將提取的LBP、HOG絕緣子的特征交給Opencv利用Adaboost算法訓(xùn)練出分類(lèi)器,然后利用分類(lèi)器模型在圖像中進(jìn)行絕緣子定位識(shí)別,對(duì)比發(fā)現(xiàn)LBP與Adaboost組合模型的絕緣子識(shí)別率最高。最后運(yùn)用此模型對(duì)大量接觸網(wǎng)圖像進(jìn)行絕緣子精確提取,利用多種經(jīng)典邊緣檢測(cè)的方法和Canny檢測(cè)算子提取出目標(biāo)絕緣子邊緣,對(duì)比發(fā)現(xiàn)Canny算子的檢測(cè)效果最好,利用閾值化方法對(duì)角度校正后的絕緣子進(jìn)行二值化,分割絕緣子和裂紋,采用連通域求面積和周長(zhǎng)的方法計(jì)算絕緣子裂紋的幾何特征,從而實(shí)現(xiàn)絕緣子裂紋檢測(cè)。在定位支座的識(shí)別中,運(yùn)用多種特征匹配的算法對(duì)定位支座進(jìn)行定位識(shí)別,發(fā)現(xiàn)SURF算子性能更好。實(shí)驗(yàn)是利用OpenCV2.4.13庫(kù)以及軟件VS2013編程,通過(guò)對(duì)大量接觸網(wǎng)圖像進(jìn)行實(shí)驗(yàn)測(cè)試,得出了 LBP與Adaboost模型在絕緣子檢測(cè)時(shí)的有效性、絕緣子裂紋分析的準(zhǔn)確性以及SURF算子對(duì)定位支座檢測(cè)的可靠性。
[Abstract]:The good working state of the contact network and its accessory parts is the basic guarantee for the safe operation of the high-speed train. Because of its long working condition, the insulator and the positioning support of the contact network are constantly affected by the electrical shock and mechanical stress. If the insulators and the positioning support fail, the contact network equipment will be damaged lightly and the weight will be straight. The sudden stop of high-speed train even causes casualties. At present, the detection of the working state of Insulators Based on image processing technology has been studied, which greatly improves the efficiency of the equipment inspection and saves a lot of manpower and financial resources. However, the previous detection methods are mainly aimed at the lack of insulators or the inclusion of foreign objects, and the rupture of insulators. There are few studies on the obstacle, and the detection of the location support is almost blank, so it is necessary to use the efficient method of machine learning in the image processing to detect the insulators and the positioning support. The image data collected by the comprehensive toured patrol vehicle is used as the original image of the sample. Using LBP and HOG to extract the local characteristics of the insulators, the classifier is trained by machine learning to extract the insulators in the image accurately, and then the insulator cracks are analyzed. Four features based on ASIFT, SURF, ORB, FREAK are used in the same time. The matching method realizes the detection of small target location support. First, the original image of insulators is preprocessed by morphological operation. A positive and negative sample library based on a large number of insulators target and non insulator image is established. Then the LBP and HOG features of insulators are extracted and the object in the image is extracted with machine learning method. Insulators. The features of the extracted LBP and HOG insulators are given to Opencv to train the classifier using the Adaboost algorithm. Then the classifier model is used to identify the insulators in the image. It is found that the insulator recognition rate of the LBP and Adaboost combination model is the highest. Finally, the model is used to insulators for a large number of contact network images. Accurate extraction, using a variety of classical edge detection methods and Canny detection operators to extract the edge of the target insulator, the contrast found that the detection effect of the Canny operator is the best. Using the threshold method, the insulator and the crack are divided into two values, the insulators and the cracks are segmented, and the area and the circumference of the connected domain are used to calculate the insulators. The geometric characteristics of the crack can be used to detect the insulator crack. In the identification of the positioning support, a variety of feature matching algorithms are used to locate the positioning support, and the SURF operator is found to be better. The experiment is to use the OpenCV2.4.13 library and software VS2013 to test the large amount of catenary images and get the LBP The validity of the Adaboost model in insulator detection, the accuracy of insulator crack analysis, and the reliability of SURF operator for locating bearing detection are also discussed.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U226.8;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 董云云;蘇鳳;孫玉梅;劉治品;楊海利;;支柱瓷絕緣子裂紋數(shù)值建模及應(yīng)力強(qiáng)度因子研究[J];電瓷避雷器;2016年06期

2 王亦森;楊圣;;基于ASIFT算法的人臉圖像特征匹配[J];工業(yè)控制計(jì)算機(jī);2016年07期

3 李宏科;;高速鐵路牽引供電系統(tǒng)6C檢測(cè)的應(yīng)用與展望[J];中國(guó)新通信;2016年13期

4 鄧紅雷;魯強(qiáng);陳力;戴棟;李富才;;基于超聲導(dǎo)波的復(fù)合絕緣子檢測(cè)[J];高電壓技術(shù);2016年04期

5 王春水;劉建屏;蘇德瑞;陳君平;季昌國(guó);;高壓支柱瓷絕緣子裂紋對(duì)振動(dòng)特性尺寸效應(yīng)研究[J];華北電力技術(shù);2015年07期

6 張菲;;基于圖像處理的懸式絕緣子串破損檢測(cè)技術(shù)[J];電力與能源;2015年01期

7 王燦進(jìn);孫濤;陳娟;;基于FREAK特征的快速景象匹配[J];電子測(cè)量與儀器學(xué)報(bào);2015年02期

8 徐征;程江艷;吳嘉敏;何為;;用于復(fù)合絕緣子傘裙老化無(wú)損檢測(cè)的單邊核磁共振方法[J];中國(guó)電機(jī)工程學(xué)報(bào);2014年36期

9 李嬋哠;牛r，

本文編號(hào)：1804868