【摘要】：隨著我國(guó)高速公路網(wǎng)的不斷擴(kuò)大,公路養(yǎng)護(hù)工作量也日益繁重。路面病害檢測(cè)作為養(yǎng)護(hù)工作的一項(xiàng)主要任務(wù)來講,也尤為重要。就目前來講,我國(guó)仍然停留在以人工檢測(cè)為主的階段,這種傳統(tǒng)的檢測(cè)方式效率低下、勞動(dòng)強(qiáng)度大、危險(xiǎn)系數(shù)高而且易造成交通擁堵現(xiàn)象,已經(jīng)明顯滿足不了高速公路路面養(yǎng)護(hù)與管理的需求。為了適應(yīng)高速公路路面養(yǎng)護(hù)自動(dòng)化管理的發(fā)展趨勢(shì),本文主要針對(duì)瀝青路面裂縫檢測(cè)系統(tǒng)做了相關(guān)的研究。 本文首先介紹了瀝青路面裂縫檢測(cè)的相關(guān)知識(shí),并對(duì)裂縫類破損的類型、形成原因和破損分級(jí)及評(píng)價(jià)標(biāo)準(zhǔn)做了詳細(xì)的闡述,在此基礎(chǔ)上,概括總結(jié)了路面裂縫自動(dòng)檢測(cè)的原理。 其次,主要探討了路面裂縫圖像的處理過程及其裂縫參數(shù)的測(cè)量算法。由于瀝青路面裂縫和背景的灰度值比較相近,需要進(jìn)行圖像增強(qiáng)處理,研究表明：基于多尺度分析的圖像增強(qiáng)算法比較適合處理路面裂縫圖像。因此,本文采用非下采樣Contourlet變換對(duì)路面裂縫圖像進(jìn)行了增強(qiáng)處理,使得裂縫特征凸顯出來,接著,對(duì)比常見邊緣檢測(cè)算法得知：傳統(tǒng)的微分邊緣檢測(cè)算子對(duì)噪聲都比較敏感,致使在檢測(cè)邊緣信息時(shí),容易出現(xiàn)假邊緣或者漏檢的現(xiàn)象。因而,本文提出了基于數(shù)學(xué)形態(tài)學(xué)的邊緣檢測(cè),并且成功地將多尺度形態(tài)學(xué)邊緣檢測(cè)算子應(yīng)用到裂縫圖像的邊緣檢測(cè)中去。實(shí)驗(yàn)表明,該算子在有效檢測(cè)裂縫邊緣的同時(shí),對(duì)椒鹽噪聲有很好地抑制能力,但是,遺憾的是,對(duì)高斯噪聲仍然比較敏感。在此基礎(chǔ)上又提出了抗噪型多尺度形態(tài)學(xué)邊緣檢測(cè)算子,此算子對(duì)椒鹽噪聲和高斯噪聲都有很好地抑制效果,而且檢測(cè)到的邊緣比較平滑,實(shí)用性好。最后使用最大類間方差法對(duì)路面裂縫圖像進(jìn)行了分割,使得裂縫和路面背景很好地分離開來,為后續(xù)的裂縫提取做好準(zhǔn)備工作。對(duì)分割出來的裂縫進(jìn)行提取并標(biāo)識(shí),將標(biāo)記出來的裂縫應(yīng)用神經(jīng)網(wǎng)絡(luò)分類器劃分為規(guī)則裂縫和不規(guī)則裂縫兩大類,然后分別采用不同的方法對(duì)裂縫的參數(shù)進(jìn)行計(jì)算。對(duì)規(guī)則裂縫的長(zhǎng)徑、短徑采用常規(guī)的幾何法計(jì)算,對(duì)不規(guī)則裂縫采用剔除法計(jì)算其面積。實(shí)驗(yàn)結(jié)果表明：本文提出的裂縫參數(shù)計(jì)算方法能夠比較精確地計(jì)算出裂縫的長(zhǎng)寬以及面積等參數(shù),而且計(jì)算精度也基本可以滿足瀝青路面裂縫檢測(cè)的要求。 最后,本文基于圖像處理技術(shù)設(shè)計(jì)了一種瀝青路面裂縫檢測(cè)系統(tǒng),并在Visual C++6.0環(huán)境下,借助Mil-Lite8.0軟件開發(fā)包對(duì)系統(tǒng)的主要功能模塊進(jìn)行了實(shí)現(xiàn),實(shí)驗(yàn)結(jié)果表明,本文設(shè)計(jì)的系統(tǒng)檢測(cè)精度高,能夠滿足路面管理要求,具有很好的實(shí)用價(jià)值。
[Abstract]:With the expansion of highway network in China, the workload of highway maintenance is becoming more and more heavy. Pavement disease detection, as a major task of maintenance, is also particularly important. At present, our country is still in the stage of manual inspection, which is inefficient, labor intensive, high risk coefficient and easy to cause traffic congestion. Has obviously not been able to meet the highway pavement maintenance and management needs. In order to adapt to the development trend of highway pavement maintenance automation management, this paper mainly focuses on the research of asphalt pavement crack detection system. This paper first introduces the relevant knowledge of asphalt pavement crack detection, and describes in detail the types, causes, classification and evaluation criteria of cracks. On this basis, it summarizes the principle of pavement crack automatic detection. Secondly, the processing process of pavement crack image and the measurement algorithm of crack parameters are discussed. Because the gray value of asphalt pavement crack and background is similar, image enhancement is needed. The research shows that the image enhancement algorithm based on multi-scale analysis is more suitable for processing pavement crack image. Therefore, the non-downsampling Contourlet transform is used to enhance the pavement crack image, which makes the crack feature prominent. Then, compared with the common edge detection algorithm, we know that the traditional differential edge detection operator is sensitive to noise. When detecting edge information, false edges or false edges are easy to appear. Therefore, in this paper, the edge detection based on mathematical morphology is proposed, and the multi-scale morphological edge detection operator is successfully applied to the edge detection of crack images. Experimental results show that the proposed operator can effectively detect crack edges and suppress salt and pepper noise, but unfortunately, it is still sensitive to Gao Si noise. On this basis, a new anti-noise multi-scale morphological edge detection operator is proposed. The operator can suppress both salt and pepper noise and Gao Si noise, and the detected edges are smooth and practical. Finally, the maximum inter-class variance method is used to segment the pavement crack image, so that the crack and the pavement background can be separated well, so as to prepare for the subsequent crack extraction. The separated cracks are extracted and marked, and the marked cracks are classified into regular cracks and irregular cracks by neural network classifier, and then the parameters of cracks are calculated by different methods. The length and short diameter of regular crack are calculated by conventional geometric method, and the area of irregular crack is calculated by eliminating method. The experimental results show that the proposed method can accurately calculate the crack parameters such as length width and area and the accuracy of calculation can basically meet the requirements of asphalt pavement crack detection. Finally, based on the image processing technology, a asphalt pavement crack detection system is designed, and the main function modules of the system are implemented with the help of Mil-Lite8.0 software development kit under the environment of Visual C 6.0. The experimental results show that, The system designed in this paper has high detection precision, can meet the requirements of pavement management, and has good practical value.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U416.217;TP391.41

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