本文選題：絕緣子 + LBP��； 參考：《西南交通大學》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)是高速列車安全運行的基本保障。由于其長時間處于工作狀態(tài),接觸網(wǎng)的絕緣子和定位支座不斷受到電氣沖擊及機械應力的影響,如果絕緣子和定位支座發(fā)生故障,輕則損壞接觸網(wǎng)設備,重則將會直接導致高速列車的驟停甚至造成人員傷亡。目前,基于圖像處理技術的絕緣子工作狀態(tài)檢測已有研究,這大大提高了設備巡檢效率,節(jié)省了不少人力財力。然而,以往的檢測方法基本是針對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,且定位支座的檢測幾乎為空白,因此有必要采用圖像處理中機器學習的高效方法檢測絕緣子和定位支座。在對以往接觸網(wǎng)及附屬裝置檢測方法、電力系統(tǒng)中絕緣子的識別進行研究以及對比人臉識別方法之后,本文利用接觸網(wǎng)綜合巡檢車采集的圖像數(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,接著采用機器學習的方法訓練分類器對圖像中的絕緣子進行精確提取,然后對絕緣子裂紋進行分析。同時采用基于ASIFT、SURF、ORB、FREAK四種特征匹配的方法實現(xiàn)了小目標定位支座的檢測。首先對絕緣子的原始圖像做了形態(tài)學運算等相關預處理,建立了以大量絕緣子目標和非絕緣子圖像為基礎的正負樣本庫,然后提取絕緣子的LBP和HOG特征。利用機器學習的方法提取圖像中的目標絕緣子。分別將提取的LBP、HOG絕緣子的特征交給Opencv利用Adaboost算法訓練出分類器,然后利用分類器模型在圖像中進行絕緣子定位識別,對比發(fā)現(xiàn)LBP與Adaboost組合模型的絕緣子識別率最高。最后運用此模型對大量接觸網(wǎng)圖像進行絕緣子精確提取,利用多種經(jīng)典邊緣檢測的方法和Canny檢測算子提取出目標絕緣子邊緣,對比發(fā)現(xiàn)Canny算子的檢測效果最好,利用閾值化方法對角度校正后的絕緣子進行二值化,分割絕緣子和裂紋,采用連通域求面積和周長的方法計算絕緣子裂紋的幾何特征,從而實現(xiàn)絕緣子裂紋檢測。在定位支座的識別中,運用多種特征匹配的算法對定位支座進行定位識別,發(fā)現(xiàn)SURF算子性能更好。實驗是利用OpenCV2.4.13庫以及軟件VS2013編程,通過對大量接觸網(wǎng)圖像進行實驗測試,得出了 LBP與Adaboost模型在絕緣子檢測時的有效性、絕緣子裂紋分析的準確性以及SURF算子對定位支座檢測的可靠性。
[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.

【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U226.8;TP391.41

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本文編號：1804868