【摘要】：隨著公路交通的快速發(fā)展,路面狀況檢測(cè)與養(yǎng)護(hù)已成為我國(guó)公路建設(shè)的首要任務(wù)。而裂縫是衡量路面質(zhì)量的重要指標(biāo)之一,因此利用數(shù)字圖像處理技術(shù)進(jìn)行路面裂縫檢測(cè)已經(jīng)成為該領(lǐng)域研究的熱點(diǎn)。在實(shí)際檢測(cè)過(guò)程中,由于路面情況復(fù)雜,使得采集的路面圖像中存在諸如油污、陰影、光照不均和隨機(jī)噪聲等干擾因素。在這類情形下,現(xiàn)有的裂縫檢測(cè)方法存在誤判和漏檢問(wèn)題,不能很好地滿足檢測(cè)需求,無(wú)法獲得較準(zhǔn)確和全面的裂縫信息,進(jìn)而不能及時(shí)對(duì)路面進(jìn)行有效的養(yǎng)護(hù)管理。針對(duì)上述問(wèn)題,本文主要從以下四個(gè)方面對(duì)復(fù)雜路面裂縫檢測(cè)進(jìn)行深入研究:路面圖像增強(qiáng)、裂縫區(qū)域提取、斷點(diǎn)連接及參數(shù)計(jì)算、裂縫檢測(cè)算法評(píng)價(jià)。(1)考慮到復(fù)雜路面裂縫圖像具有干擾噪聲多、陰影和光照不均等特征,本文提出了基于小波分析的路面裂縫圖像增強(qiáng)算法。采用非線性變換對(duì)低頻分量進(jìn)行增強(qiáng)處理,同時(shí)對(duì)高頻分量進(jìn)行小波閾值去噪處理,抑制高頻部分的噪聲信息,然后通過(guò)小波重構(gòu)得到增強(qiáng)后的圖像。實(shí)驗(yàn)結(jié)果表明該算法不僅很好地增強(qiáng)了路面圖像的對(duì)比度,而且在抑制噪聲的同時(shí)能夠最大限度地保留裂縫的邊緣細(xì)節(jié)。(2)針對(duì)背景模糊、光照不均及陰影等復(fù)雜路面裂縫圖像,本文提出了一種基于Legendre矩和分?jǐn)?shù)階積分的路面裂縫提取方法,首先通過(guò)Legendre矩找到最佳的類似于參考圖像的連通域,即找到最優(yōu)的分?jǐn)?shù)階積分階次;然后利用一個(gè)最優(yōu)階次的分?jǐn)?shù)階積分掩膜來(lái)處理圖像,使圖像中像素的灰度級(jí)減少;最后計(jì)算分?jǐn)?shù)階積分算子處理后圖像的直方圖,根據(jù)直方圖確定最優(yōu)閾值以便提取裂縫信息。該方法充分地利用了分?jǐn)?shù)階積分的性質(zhì),考慮了像素的空間分布,增加了圖像的均勻性,不但去除了大量的噪聲干擾點(diǎn),而且較完整地提取出裂縫區(qū)域。(3)對(duì)于所提取的裂縫不連續(xù)、斷裂等現(xiàn)象,本文采用一種基于區(qū)域搜尋的裂縫連接算法進(jìn)行裂縫斷點(diǎn)連接,先依據(jù)深度優(yōu)先原則進(jìn)行裂縫鄰域搜索,再根據(jù)連通性原理去除斷點(diǎn)。并對(duì)連接后的裂縫進(jìn)行長(zhǎng)度和寬度的測(cè)量,分別采用骨架提取法和二階矩Ferret算法對(duì)裂縫長(zhǎng)度和寬度進(jìn)行測(cè)量分析。(4)為了驗(yàn)證本文檢測(cè)算法的性能,分別從準(zhǔn)確率、完整率和F-測(cè)度三個(gè)方面進(jìn)行評(píng)價(jià)。實(shí)驗(yàn)中,針對(duì)背景較均勻、對(duì)比度低及有塊狀陰影等復(fù)雜路面裂縫圖像,將本文算法分別與大津閾值分割、Canny邊緣檢測(cè)、最小生成樹(shù)算法、K均值聚類算法和模糊C均值聚類算法進(jìn)行對(duì)比分析。實(shí)驗(yàn)結(jié)果表明本文算法檢測(cè)的準(zhǔn)確率、完整率和F-測(cè)度值均較高,進(jìn)一步驗(yàn)證了本文算法具有較好的適用性。
[Abstract]:With the rapid development of highway traffic, pavement condition detection and maintenance has become the primary task of highway construction in China. Crack is one of the most important indexes to measure the pavement quality, so the detection of pavement crack by digital image processing technology has become a hot spot in this field. In the actual detection process, because of the complexity of the road surface, there are some interference factors such as oil pollution, shadow, uneven illumination and random noise in the collected road surface images. In such cases, the existing crack detection methods have the problems of misjudgment and missed detection, which can not meet the needs of detection, can not obtain more accurate and comprehensive crack information, and can not effectively maintain and manage the pavement in time. In view of the above problems, this paper mainly studies the crack detection of complex pavement from the following four aspects: road image enhancement, crack area extraction, breakpoint connection and parameter calculation. The evaluation of crack detection algorithm. (1) considering that the image of complex pavement crack has many features such as interference noise, shadow and uneven illumination, this paper proposes an enhancement algorithm of pavement crack image based on wavelet analysis. The low frequency component is enhanced by nonlinear transformation, and the high frequency component is de-noised by wavelet threshold to suppress the noise information of the high frequency part, and then the enhanced image is obtained by wavelet reconstruction. The experimental results show that the proposed algorithm not only enhances the contrast of pavement images, but also can keep the edge details of cracks as much as possible while suppressing noise. (2) aiming at the background blur, In this paper, a method of pavement crack extraction based on Legendre moment and fractional integral is proposed. Firstly, the best connected region similar to the reference image is found by Legendre moment. That is to find the best fractional integral order; Then a fractional integral mask of the optimal order is used to process the image, which reduces the gray level of the pixels in the image. Finally, the histogram of the image processed by the fractional integral operator is calculated, and the optimal threshold is determined according to the histogram to extract the crack information. This method makes full use of the property of fractional integral, considers the spatial distribution of pixels, increases the uniformity of image, and not only removes a large number of noise interference points, Moreover, the fracture area is extracted completely. (3) for the discontinuity and fracture of the extracted cracks, this paper adopts a fracture connection algorithm based on the region search to connect the fracture breakpoints. The crack neighborhood is searched according to the principle of depth first, and the breakpoint is removed according to the principle of connectivity. The length and width of the connected cracks are measured, and the crack length and width are measured and analyzed by skeleton extraction method and second moment Ferret algorithm. (4) in order to verify the performance of the proposed algorithm, the accuracy of the proposed algorithm is analyzed. Integrity rate and F-measure were evaluated in three aspects. In the experiment, aiming at the images of complex pavement cracks, such as uniform background, low contrast and block shadow, the algorithm of this paper is segmented with Otsu threshold, Canny edge detection, minimum spanning tree algorithm, etc. K-means clustering algorithm and fuzzy C-means clustering algorithm are compared and analyzed. The experimental results show that the accuracy, integrity rate and F- measure value of the proposed algorithm are high, which further verifies the applicability of the proposed algorithm.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U418.6;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫英慧;孫英娟;;基于維納濾波的圖像還原研究[J];長(zhǎng)春師范大學(xué)學(xué)報(bào);2016年10期

2 葉青;胡昌標(biāo);;一種改進(jìn)的基于圖論的圖像分割方法[J];計(jì)算機(jī)與現(xiàn)代化;2016年09期

3 陳建平;秦斌;王欣;;非均勻光照?qǐng)D像的自適應(yīng)閾值分割[J];湖南工業(yè)大學(xué)學(xué)報(bào);2016年04期

4 方明;李洪娜;雷立宏;梁銘;;低照度視頻圖像增強(qiáng)算法綜述[J];長(zhǎng)春理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年03期

5 李?yuàn)檴?趙春娜;關(guān)永;施智平;王瑞;李曉娟;葉世偉;;分?jǐn)?shù)階微積分定義的一致性在HOL4中的驗(yàn)證[J];計(jì)算機(jī)科學(xué);2016年03期

6 高銀;云利軍;石俊生;丁慧梅;;基于各向異性高斯濾波的暗原色理論霧天彩色圖像增強(qiáng)算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2015年09期

7 劉晟;王衛(wèi)星;曹霆;楊楠;楊洋;;基于差分計(jì)盒維數(shù)及最大熵閾值的裂縫提取[J];長(zhǎng)安大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年05期

8 錢彬;唐振民;徐威;;基于稀疏自編碼的路面裂縫檢測(cè)[J];北京理工大學(xué)學(xué)報(bào);2015年08期

9 張建旭;蔣燕;劉興國(guó);;基于深度優(yōu)先反向搜索算法確定有效路徑集合[J];重慶交通大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年03期

10 彭博;蔣陽(yáng)升;韓世凡;羅楠欣;;路面裂縫圖像自動(dòng)識(shí)別算法綜述[J];公路交通科技;2014年07期

相關(guān)碩士學(xué)位論文 前4條

1 許素素;改進(jìn)的模糊C均值聚類算法在圖像分割中的應(yīng)用[D];長(zhǎng)安大學(xué);2015年

2 張東升;車載式路面破損檢測(cè)照明系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];長(zhǎng)安大學(xué);2015年

3 郝愛(ài)枝;瀝青路面裂縫檢測(cè)系統(tǒng)研究[D];太原理工大學(xué);2014年

4 徐兵;基于圖像處理技術(shù)的橋梁病害檢查和裂縫測(cè)量研究[D];長(zhǎng)安大學(xué);2009年

，

本文編號(hào)：2415716