本文選題：高速公路檢測　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,我國總公路里程數(shù)已經(jīng)超過美國成為世界第一。隨之而來的是我國公路的大規(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,線性里程樁號定位方法存在著路面管理單元的劃分過大,不能精確的定位路面病害的問題,無法實(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題,以及里程樁號的斷鏈問題,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定位不準(zhǔn)確的問題。但是路元以其分布密度大、里程長、面積小等特點(diǎn)對路元信息的采集與識別工作提出了挑戰(zhàn),目前路元信息的自動(dòng)化采集與識別的研究尚未有人涉及。本文針對路元信息提出了一個(gè)快速高效的采集方案,并針對采集到的路元圖像,提出了基于雙閾值檢測的路元標(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)的基于邊緣檢測的定位方法。最后,針對路元標(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)完成對的路元字符的識別,該方法能夠有效的識別部分缺損、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,并采用上述方法對路元標(biāo)志進(jìn)行定位和識別,其定位精度達(dá)到95%,識別精度能夠達(dá)到92.5%。通過實(shí)驗(yàn)證明,本文提出的定位識別方法高效可行,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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U418.4;TP391.41

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