發(fā)布時間：2018-02-28 04:35

  本文關(guān)鍵詞： 儀表識別 結(jié)構(gòu)化支持向量機 SURF特征 預(yù)建模 KNN　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科學(xué)技術(shù)的發(fā)展,越來越多的領(lǐng)域開始使用智能系統(tǒng)。儀表識別技術(shù)作為一種智能處理技術(shù),已被廣泛地應(yīng)用在工業(yè)領(lǐng)域上,且越來越受到人們的關(guān)注。雖然國內(nèi)外學(xué)者對固定式儀表識別已經(jīng)有了大量的研究,但是對于非固定式儀表識別的研究還非常少。本文研發(fā)了基于智能機器人的非固定式儀表示數(shù)識別系統(tǒng),它通過機器人搭載的攝像頭獲得儀表圖像,并實現(xiàn)多類儀表的示數(shù)識別。本文主要工作包括儀表識別的算法研究和面向電力領(lǐng)域中非固定儀表識別的多類別儀表示數(shù)識別系統(tǒng)的研發(fā)。為解決非固定儀表識別中存在的儀表位置的隨機性問題,同時降低儀表識別難度,提高識別率,本文提出了基于儀表檢測、儀表配準和儀表識別的計算模型。對于多類型的儀表識別難題,本文設(shè)計了基于儀表建模的識別方法,降低了識別算法的復(fù)雜性,提高了識別算法的精度和魯棒性,并實現(xiàn)了系統(tǒng)的通用性和兼容性。同時本文還對儀表識別算法中的若干方法做了相應(yīng)的改進。本文首先提出了一種基于結(jié)構(gòu)化支持向量機的儀表檢測算法。該算法使用結(jié)構(gòu)化支持向量機作為分類器,充分利用了目標與背景中其他物體的幾何關(guān)系,提高了檢測準確率。同時算法使用限制對比度自適應(yīng)直方圖均衡的方法進行圖像預(yù)處理,有利于減少光照等環(huán)境因素對圖像的影響。其次,本文提出了基于SURF特征的圖像配準算法。該算法通過SURF特征和BF算法進行特征匹配,使用PROSAC算法進行匹配對篩選。同時本文在特征點匹配算法上做了一定的改進,包括限制配準區(qū)域和對匹配對的預(yù)篩選。圖像配準算法是后續(xù)識別算法的依賴,對降低識別難度,提高識別精確度有較大的幫助。接著,本文針對兩類常用的儀表類型提出了識別算法。其中指針式儀表識別基于預(yù)建模算法和圖像旋轉(zhuǎn)法。預(yù)建模算法充分利用了儀表模板的先驗信息,降低了指針檢測的難度。圖像旋轉(zhuǎn)法通過旋轉(zhuǎn)圖像后在水平區(qū)域提取匹配樣本,相比直接旋轉(zhuǎn)搜索窗口,降低了難度和計算量。數(shù)顯式儀表識別算法通過KNN分類器識別數(shù)字。該算法充分利用先驗信息進行數(shù)顯區(qū)域的傾斜矯正和數(shù)字粗略定位,提高了數(shù)字分割的準確性。算法同時提出了使用輪廓和凸包的關(guān)系單獨識別小數(shù)點的方法,相比直接將小數(shù)點進行分類識別有更好的效果。最后,針對與大立公司的合作項目,本文研制了基于智能機器人的儀表示數(shù)識別系統(tǒng),該系統(tǒng)由儀表建模軟件和儀表識別算法組成,完整地實現(xiàn)了智能機器人從預(yù)置點停下后進行檢測、配準、識別的整個過程,具有較高的準確率和速度。該儀表示數(shù)識別系統(tǒng)已得到實際應(yīng)用。
[Abstract]:With the development of science and technology, more and more fields begin to use intelligent system. As an intelligent processing technology, instrument recognition technology has been widely used in the industrial field. And more and more people pay attention to it. Although scholars at home and abroad have done a lot of research on fixed instrument recognition, However, there are few researches on the recognition of non-stationary instruments. In this paper, an intelligent robot based non-stationary instrument representation recognition system is developed, which obtains the instrument image through the camera of the robot. The main work of this paper includes the algorithm research of instrument recognition and the research and development of multi-class instrument representation number recognition system for non-fixed instrument recognition in electric power field. In order to solve the problem of non-fixed instrument recognition. The randomness of the location of the instrument, At the same time, the difficulty of instrument identification is reduced and the recognition rate is improved. This paper presents a calculation model based on instrument detection, instrument registration and instrument recognition. The complexity of the recognition algorithm is reduced, and the accuracy and robustness of the recognition algorithm are improved. At the same time, some methods of instrument recognition algorithm are improved. Firstly, a new instrument detection algorithm based on structured support vector machine is proposed. Method using structured support vector machine as classifier, The geometric relationship between the object and other objects in the background is fully utilized, and the detection accuracy is improved. At the same time, the algorithm uses the method of constrained contrast adaptive histogram equalization for image preprocessing. It is helpful to reduce the influence of environmental factors such as illumination on the image. Secondly, an image registration algorithm based on SURF features is proposed. The algorithm uses SURF feature and BF algorithm to match the image. At the same time, this paper makes some improvements in the feature point matching algorithm, including limiting the registration region and pre-screening matching pairs. Image registration algorithm is dependent on the subsequent recognition algorithm and reduces the difficulty of recognition. It is helpful to improve the accuracy of recognition. Then, In this paper, a recognition algorithm is proposed for two kinds of instrument types, which are based on pre-modeling algorithm and image rotation method. The pre-modeling algorithm makes full use of the prior information of the instrument template. It reduces the difficulty of pointer detection. The image rotation method extracts matching samples in the horizontal region after rotating the image, compared with the direct rotation search window. The digital display instrument recognition algorithm uses KNN classifier to recognize the number. The algorithm makes full use of prior information to correct the tilt of the digital display region and locate the digital roughly. At the same time, the method of using the relationship between contour and convex hull to identify decimal points separately is proposed, which is more effective than the classification and recognition of decimal points directly. Finally, for the cooperative project with Dali Company, In this paper, an instrument representation recognition system based on intelligent robot is developed. The system is composed of instrument modeling software and instrument recognition algorithm. The whole process of intelligent robot detection, registration and recognition after stopping from preset point is realized. The system has been applied in practice.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242

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