發(fā)布時間：2018-02-27 03:38

  本文關鍵詞： 裂縫檢測 圖像預處理 圖像分割 特征提取 支持向量機　出處：《長安大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著經(jīng)濟的發(fā)展，我國公路交通事業(yè)高速發(fā)展，因此，對公路的養(yǎng)護工作也提出了更高的要求。公路在建成后受到氣候、地質條件、通行量、載荷量等因素的影響，導致公路產(chǎn)生不同程度的裂縫，因此，相關部門需要對公路進行定期的檢測和養(yǎng)護。國內在對路面裂縫檢測時大部分還是使用傳統(tǒng)的人工檢測方法，但這種傳統(tǒng)的方法效率低、誤差大，而且對交通有較大的影響，檢測人員的人身安全也不能完全保證。因此，對路面自動檢測技術的研究迫在眉睫，以便于節(jié)省費用，，延長使用年限，提高公路的服務水平。 本文主要研究裂縫圖像的處理技術，分為圖像預處理、圖像分割、特征提取三部分。圖像預處理部分，本文采用最近鄰插值法將圖像縮小，變?yōu)樵瓐D像的1/4；采用四種不同類型的結構元素依次對路面裂縫圖像進行中值濾波，平滑去噪；采用基于圖像背景提取的灰度校正算法校正圖像光照不均。圖像分割部分，本文采用Ostu閾值分割算法對路面裂縫圖像進行分割，并進行適當改善；采用連通域白色像素點閾值去噪算法去除二值圖像的噪聲；將數(shù)學形態(tài)學和白色像素點閾值去噪算法相結合，利用多種形態(tài)學算法交替處理，提取出裂縫，最后運用迭代細化方法對裂縫進行了細化。特征提取部分，本文根據(jù)不同類型裂縫的特征選取了裂縫像素面積、水平投影、垂直投影、矩形度作為裂縫的特征值，利用裂縫像素面積能夠準確判斷圖像中有無裂縫。 本文用裂縫的四個屬性作為支持向量機分類器的特征向量，采用高斯徑向基核函數(shù)RBF，運用“一對多”的多分類算法，對95幅測試樣本進行識別，總識別率為85.26%。最后簡要分析了造成誤判的原因。在相同樣本條件下，對比了BP神經(jīng)網(wǎng)絡和支持向量機的分類效果，結果表明，支持向量機的分類精確度要優(yōu)于BP神經(jīng)網(wǎng)絡。 為了滿足道路養(yǎng)護對路面裂縫數(shù)據(jù)參數(shù)的需求，分別計算了橫向和縱向裂縫的長度以及塊狀和網(wǎng)狀裂縫的最小外接矩形面積，進一步計算了路面狀況指數(shù)PCI，得出路面破損的程度等級，從而確定養(yǎng)護策略。
[Abstract]:With the development of economy, the highway traffic in our country is developing rapidly. Therefore, the maintenance of highway is also required. The highway is affected by the factors of climate, geological conditions, traffic volume, load and so on. As a result of varying degrees of cracks in the highway, the relevant departments need to carry out regular inspection and maintenance of the highway. Most of the traditional manual methods are still used in the detection of pavement cracks in China, but this traditional method is inefficient. The error is large, and it has a great influence on traffic, and the personal safety of the examiner can not be completely guaranteed. Therefore, it is urgent to study the automatic detection technology of road surface in order to save the cost and prolong the service life. Improve the service level of the highway. This paper mainly studies the processing technology of crack image, which is divided into three parts: image preprocessing, image segmentation and feature extraction. Changing to 1 / 4 of the original image; adopting four different structural elements to filter the pavement crack image in turn to smooth the noise; using the gray correction algorithm based on the image background extraction to correct the uneven illumination of the image. In this paper, the Ostu threshold segmentation algorithm is used to segment the pavement crack image, and the white pixel threshold de-noising algorithm in connected domain is used to remove the noise of the binary image. Combining mathematical morphology with white pixel threshold denoising algorithm, the cracks are extracted by alternate processing of various morphological algorithms, and the cracks are refined by iterative thinning method. In this paper, the crack pixel area, horizontal projection, vertical projection and rectangular degree are selected as the characteristic values of cracks according to the characteristics of different types of cracks, and the crack pixel area can be used to accurately judge whether there are cracks in the image. In this paper, four attributes of cracks are used as feature vectors of SVM classifier, Gao Si radial basis function RBFand "one-to-many" multi-classification algorithm are used to identify 95 test samples. The total recognition rate is 85.26. Finally, the causes of misjudgment are briefly analyzed. Under the same sample condition, the classification results of BP neural network and support vector machine are compared. The results show that the classification accuracy of support vector machine is better than that of BP neural network. In order to meet the requirement of pavement crack data parameters for road maintenance, the length of transverse and longitudinal cracks and the minimum external rectangular area of block and mesh cracks are calculated, respectively. Furthermore, the pavement condition index PCI is calculated, and the degree of pavement damage is obtained, and the maintenance strategy is determined.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.41;U418

【參考文獻】

相關期刊論文 前7條

1 孫慧,周紅霞,李朝暉;圖象處理中邊緣檢測技術的研究[J];電腦開發(fā)與應用;2002年10期

2 楊薇;趙冬梅;;基于VC++的鄰域均值濾波法圖像增強[J];保山學院學報;2011年05期

3 劉真;用形態(tài)學方法進行數(shù)字圖象的分析和處理[J];華北電力大學學報;1996年03期

4 聞莎,游志勝;性能優(yōu)化的同態(tài)濾波空域算法[J];計算機應用研究;2000年03期

5 梁琳;何衛(wèi)平;雷蕾;張維;王紅霄;;光照不均圖像增強方法綜述[J];計算機應用研究;2010年05期

6 喻翔,彭其淵;公路路面檢測技術的發(fā)展[J];交通標準化;2004年12期

7 啜二勇;;國外路面自動檢測系統(tǒng)發(fā)展綜述[J];交通標準化;2009年17期

本文編號：1540983