發(fā)布時(shí)間：2018-04-05 13:37

  本文選題：高速公路檢測(cè)　切入點(diǎn)：路面管理系統(tǒng)　出處：《湖北工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著我國(guó)的交通運(yùn)輸行業(yè)地不斷發(fā)展,公路不斷地建設(shè),公路里程數(shù)也不斷地增加。目前,我國(guó)總公路里程數(shù)已經(jīng)超過(guò)美國(guó)成為世界第一。隨之而來(lái)的是我國(guó)公路的大規(guī)模養(yǎng)護(hù),為了完成公路大規(guī)模的管理養(yǎng)護(hù)工作,路面管理養(yǎng)護(hù)系統(tǒng)應(yīng)運(yùn)而生,該系統(tǒng)使用線性里程樁號(hào)的方法定位路面病害,并記錄路面病害的發(fā)展與維修歷史。然而,線性里程樁號(hào)定位方法存在著路面管理單元的劃分過(guò)大,不能精確的定位路面病害的問(wèn)題,無(wú)法實(shí)現(xiàn)養(yǎng)護(hù)路面的精細(xì)化管理。為了解決路面病害不能精確定位的問(wèn)題,以及里程樁號(hào)的斷鏈問(wèn)題,路元概念被提出并得到了較好應(yīng)用。路元是最小路面管理單元,該理念的提出可以科學(xué)有效地解決里程樁號(hào)定位不準(zhǔn)確的問(wèn)題。但是路元以其分布密度大、里程長(zhǎng)、面積小等特點(diǎn)對(duì)路元信息的采集與識(shí)別工作提出了挑戰(zhàn),目前路元信息的自動(dòng)化采集與識(shí)別的研究尚未有人涉及。本文針對(duì)路元信息提出了一個(gè)快速高效的采集方案,并針對(duì)采集到的路元圖像,提出了基于雙閾值檢測(cè)的路元標(biāo)志定位方法,該方法是利用圖像的灰度直方圖將路元圖像同時(shí)進(jìn)行高、低兩個(gè)閾值的處理,并將處理后的圖像進(jìn)行疊加,得到路元的定位圖像。該方法中高低閾值的確定是其關(guān)鍵部分。通過(guò)實(shí)驗(yàn)證明,基于雙閾值的路元定位方法定位準(zhǔn)確率高于傳統(tǒng)的基于邊緣檢測(cè)的定位方法。最后,針對(duì)路元標(biāo)志中字符性變大,干擾多等特點(diǎn),提出了改進(jìn)的高斯濾波方法,能夠有效的濾除雜質(zhì)并增強(qiáng)字符邊緣信息,并利用BP神經(jīng)網(wǎng)絡(luò)完成對(duì)的路元字符的識(shí)別,該方法能夠有效的識(shí)別部分缺損、污染的字符。本文選取1000張?jiān)谀呈「咚俟飞喜杉降穆吩獔D像,并采用上述方法對(duì)路元標(biāo)志進(jìn)行定位和識(shí)別,其定位精度達(dá)到95%,識(shí)別精度能夠達(dá)到92.5%。通過(guò)實(shí)驗(yàn)證明,本文提出的定位識(shí)別方法高效可行,系統(tǒng)工作運(yùn)行安全可靠,為高速公路的快速檢測(cè)提供了有效的方法和手段。
[Abstract]:With the development of transportation industry and the construction of highway, the mileage of highway is increasing.At present, China's total road mileage has surpassed the United States to become the first in the world.The following is the large-scale maintenance of highway in our country. In order to complete the large-scale management and maintenance of highway, the pavement management and maintenance system emerges as the times require, and the system uses the method of linear mileage pile number to locate the pavement disease.The development and maintenance history of pavement diseases were recorded.However, the linear mileage pile number location method has the problem that the pavement management unit is too large to accurately locate the pavement disease, and the fine management of the maintenance pavement can not be realized.In order to solve the problem that the road diseases can not be located accurately and the mileage pile number is broken, the concept of road element has been put forward and applied well.The road element is the minimum pavement management unit, which can solve the problem of inaccurate location of mileage pile number scientifically and effectively.However, because of its large distribution density, long mileage and small area, road elements have challenged the acquisition and recognition of road element information. At present, the research on automatic collection and recognition of road element information has not been involved.In this paper, a fast and efficient method for road element information acquisition is proposed, and a road element sign location method based on dual threshold detection is proposed for the collected road element image.This method uses the gray histogram of the image to process the road element image at the same time with two thresholds: high and low, and the processed image is superposed to obtain the location image of the road element.The determination of high and low threshold is the key part of this method.The experimental results show that the accuracy of road element localization based on double threshold is higher than that of traditional edge detection.Finally, in view of the character character becoming bigger and the interference more in the road element sign, the improved Gao Si filter method is put forward, which can effectively filter the impurity and enhance the character edge information, and use the BP neural network to complete the recognition of the road element character.This method can effectively identify some defective and contaminated characters.In this paper, 1000 road element images collected on highway in a province are selected, and the above methods are used to locate and identify the road element signs. The positioning accuracy is 95% and the recognition accuracy can reach 92.5%.The experimental results show that the proposed method is efficient and feasible, and the system is safe and reliable in operation, which provides an effective method and means for the rapid detection of freeway.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U418.4;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蔣樹強(qiáng);閔巍慶;王樹徽;;面向智能交互的圖像識(shí)別技術(shù)綜述與展望[J];計(jì)算機(jī)研究與發(fā)展;2016年01期

2 胡曉光;程承旗;李德仁;;基于視覺(jué)認(rèn)知的禁令交通標(biāo)志檢測(cè)[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年06期

3 梁利利;王興武;;基于Sobel算子邊緣檢測(cè)和數(shù)學(xué)形態(tài)學(xué)的車牌定位算法[J];現(xiàn)代電子技術(shù);2015年14期

4 史冀波;徐業(yè)飛;;ARRB多功能道路檢測(cè)車在路況檢測(cè)中應(yīng)用與探討[J];北方交通;2014年09期

5 劉萌;;圖像處理在車牌識(shí)別技術(shù)中的應(yīng)用[J];電子測(cè)試;2014年18期

6 閆偉;;車牌識(shí)別系統(tǒng)中邊緣檢測(cè)及算法研究[J];電腦開發(fā)與應(yīng)用;2014年07期

7 王永杰;裴明濤;賈云得;;多信息融合的快速車牌定位[J];中國(guó)圖象圖形學(xué)報(bào);2014年03期

8 琚曉輝;徐凌;;國(guó)內(nèi)外道路路況檢測(cè)車現(xiàn)狀分析與比較[J];公路交通科技(應(yīng)用技術(shù)版);2014年03期

9 楊華;衣燕慧;劉國(guó)東;石祥濱;;拖影情況下快速飛行乒乓球體的實(shí)時(shí)識(shí)別與跟蹤[J];沈陽(yáng)航空航天大學(xué)學(xué)報(bào);2014年01期

10 江進(jìn);;基于灰度化及邊緣檢測(cè)算法的車牌識(shí)別技術(shù)研究[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2014年01期

相關(guān)博士學(xué)位論文 前5條

1 劉志明;長(zhǎng)焦距斜視航空相機(jī)圖像拖影與扭曲補(bǔ)償技術(shù)研究[D];中國(guó)科學(xué)院研究生院(長(zhǎng)春光學(xué)精密機(jī)械與物理研究所);2014年

2 王剛毅;交通標(biāo)志檢測(cè)與分類算法研究[D];哈爾濱工業(yè)大學(xué);2013年

3 曾俊;圖像邊緣檢測(cè)技術(shù)及其應(yīng)用研究[D];華中科技大學(xué);2011年

4 蔣紅妍;高速公路路面養(yǎng)護(hù)管理系統(tǒng)研究[D];長(zhǎng)安大學(xué);2006年

5 張航;自然場(chǎng)景下的交通標(biāo)志識(shí)別算法研究[D];中南大學(xué);2006年

相關(guān)碩士學(xué)位論文 前10條

1 郜偉;復(fù)雜環(huán)境下車牌定位的研究與應(yīng)用[D];安徽大學(xué);2016年

2 湯玉W，

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