本文關(guān)鍵詞：基于無人機的路橋病害檢測方法研究　出處：《中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 遙感 無人機 目標檢測 路橋病害 信息提取

【摘要】：路橋病害的實時檢測對于路橋的安全具有重要意義,也是公路巡檢和維護工作的主要依據(jù)之一。路橋病害的數(shù)量和嚴重程度直接反映了路橋健康狀況,同時,路橋病害的改變預(yù)示著路橋表層或者深層的病變,進而影響路橋的使用壽命和安全系數(shù)。線狀病害、網(wǎng)狀病害和坑洞作為路橋早期病害,是路橋表面的常見病害,其相關(guān)參數(shù)(如線狀裂縫的形狀、面積和位置等)是評估路橋健康狀況的重要參數(shù)。傳統(tǒng)路橋檢測是采用路橋檢測車加巡檢員目視相結(jié)合的方法,具有檢測效率低下、費時費力的缺點,而無人機作為新型的檢測平臺,具有采集影像角度靈活、成本低、效率高的特點。隨著低空攝影測量和圖像處理技術(shù)的發(fā)展,路橋病害的檢測方法產(chǎn)生了顯著變化,特別是無人機的發(fā)展對整個路橋病害檢測體系產(chǎn)生了深刻影響。因此,研究基于無人機的路橋病害檢測方法及其精度評估,對于路橋健康狀況的評估和對應(yīng)的巡檢方案的安排都具有重要意義。本研究基于路橋養(yǎng)護公司提供的路橋病害數(shù)據(jù)和大疆S900無人機搭載的SONY ILCE-7R相機拍攝得到的影像數(shù)據(jù),首先采用無人機影像處理降低無人機影像模糊和影像高噪聲,然后應(yīng)用手持相機拍攝得到的路橋病害影像訓(xùn)練線狀裂縫、網(wǎng)狀裂縫和坑洞這三類早期和基礎(chǔ)性的路橋病害的多部件形變模型,然后應(yīng)用路橋部件的多部件形變模型在無人機影像上,通過檢測得到路橋病害區(qū)域,再基于路橋病害區(qū)域影像應(yīng)用邊緣檢測方法提取出路橋病害信息,最后對基于無人機影像的路橋病害檢測系統(tǒng)進行了介紹和實力驗證。通過分析無人機影像處理、路橋病害模型、模型檢測和路橋信息提取方法和路橋病害檢測系統(tǒng),本研究得出了以下結(jié)論:(1)提出了一套創(chuàng)新性的基于無人機的路橋病害檢測技術(shù)。本研究針對無人機影像特性比較了多種影像處理算法以提高影像質(zhì)量;驗證了描述路橋病害的形態(tài)、結(jié)構(gòu)、紋理特性的多部件形變模型,及其模擬路橋病害的可行性,并建立了路橋病害模型庫;檢測出路橋病害影像區(qū)域并采用邊緣檢測算法來提取出路橋病害信息,顯著提高了路橋病害檢測和信息提取的效果;最后闡述了路橋病害檢測系統(tǒng)功能、算法和實施驗證。(2)通過研究無人機影像特性和影像預(yù)處理方法,有效改善了無人機影像質(zhì)量。在多參數(shù)條件下比較了去模糊概率模型和標準各向異性擴散這兩種算法,證明了標準各向異性擴散函數(shù)在保存影像邊緣特征的前提下,在消除無人機影像模糊方面表現(xiàn)更好。(3)通過研究路橋病害影像特性和特征表達方法,創(chuàng)新性地提出將模擬了路橋病害影像形態(tài)、結(jié)構(gòu)、紋理特性的多部件形變模型應(yīng)用到路橋病害檢測中,并比較了病害模型的模擬泛化性能。研究結(jié)果表明,線狀裂縫模型模擬泛化能力良好,模型的平均精度AP=0.64891;網(wǎng)狀裂縫模型模擬泛化能力較差,平均精度AP=0.5658;坑洞模型模型的泛化能力最強,平均精度AP=0.68254。在盡量全面地提取出路橋病害的要求下,如果要求路橋病害檢準率較高,當召回率(recall)值在0.6時,精度(precision)值均在0.2~0.4之間。(4)不同病害模型應(yīng)用在無人機影像上,在響應(yīng)度閾值的設(shè)定和檢測效果上存在差異,邊緣分割效果也具有顯著差別。研究結(jié)果表明,線狀裂縫響應(yīng)度為-0.95時查全率最高,網(wǎng)狀裂縫查全率最高出現(xiàn)在響應(yīng)度為-0.99處,坑洞響應(yīng)度為-0.9時查全率最高。對檢出區(qū)域應(yīng)用局部自適應(yīng)閾值分割和最小連通域去除法提取路橋病害的邊緣、形狀等參數(shù)信息,與經(jīng)典的Canny算法、Sobel算法相比,本實驗提出的思路可以更準確地提取出路橋病害信息。(5)通過編程實現(xiàn)基于無人機影像的路橋病害檢測系統(tǒng),在系統(tǒng)設(shè)計、功能實現(xiàn)和實例驗證方面進行了闡述。實現(xiàn)結(jié)果表明,本文算法有效可靠、系統(tǒng)功能良好,具有良好的應(yīng)用前景。
[Abstract]:The real-time detection of Luqiao disease has important significance for the safety of Luqiao, is one of the main basis for the highway inspection and maintenance work in Luqiao. The number and severity of disease directly reflects the health status of Luqiao, at the same time, Luqiao disease change indicates that Luqiao surface or deep lesions, thereby affecting Luqiao's service life and the safety coefficient of linear. Disease, disease and potholes as reticular early disease is a common disease in Luqiao, Luqiao on the surface of the relevant parameters (such as linear crack shape, size and location) is an important parameter to evaluate the health status of Luqiao. The traditional detection method is adopted to detect the Luqiao Luqiao car with a combination of visual inspection, has low detection efficiency and the shortcomings of time-consuming, and as a new platform for UAV detection, image acquisition with flexible angle, low cost and high efficiency. With the characteristics of low altitude photography The development of measurement and image processing technology, detection method of Luqiao disease has changed significantly, especially the development of UAV has a profound impact on the whole of Luqiao disease detection system. Therefore, the research of Luqiao disease detection method and accuracy assessment based on UAV, is of great significance to assess the inspection program in Luqiao health and the corresponding arrangement. This study based on the image data of UAV with Luqiao disease data of Luqiao and Xinjiang maintenance company provides S900 SONY ILCE-7R camera was first used, UAV image processing to reduce the UAV blurred images and images with high noise, linear crack image training in Luqiao disease and then apply the hand-held camera to get. Multi component deformation model of mesh cracks and potholes of these three types of early and basic diseases in Luqiao, Luqiao and many parts of parts of the application form In the model of UAV images, obtained by detecting the disease region of Luqiao, Luqiao area image detection method and application of edge extraction based on the disease of bridge disease information, at the end of the Luqiao disease detection system based on UAV images are introduced and the strength verification. Through the analysis of the UAV image processing, Luqiao model and Luqiao model of disease, detection the information extraction method and Luqiao disease detection system, this study draws the following conclusions: (1) put forward a set of innovative Luqiao disease detection technology based on UAV. Based on the characteristics of UAV image comparison of a variety of image processing algorithms to improve image quality; verify the description of Luqiao disease morphology, structure, multi component deformation model of texture characteristic, and the feasibility of simulation of Luqiao disease, and established the Luqiao disease model library; detection of bridge disease image and mining area Using edge detection algorithm to extract the bridge disease information, improve the Luqiao disease detection and information extraction; finally elaborated Luqiao disease detection system function, algorithm and implementation of the verification. (2) through the research on the characteristic of a UAV image and image preprocessing method, effectively improve the UAV image quality in multi parameter. Under the condition of comparison to diffusion of the two algorithms of fuzzy probability model and standard anisotropy, proved that the standard anisotropic diffusion function in the premise of preserving image edge features, in the elimination of UAV image fuzzy performance better. (3) the expression method through the study of Luqiao disease image features and characteristics, put forward the simulation Luqiao disease image morphology, structure, texture and multi component deformation model is applied to Luqiao disease detection, and compared the simulation model of disease generalization performance. The results table The linear crack model generalization ability is good, the average accuracy of AP=0.64891 model; mesh crack model generalization ability is poor, the average accuracy of AP=0.5658; the generalization ability of the model pits the strongest, the average accuracy of AP=0.68254. in the extraction of the bridge as a comprehensive disease request, if Luqiao disease rate is higher when the precision, recall rate (recall) value of 0.6, accuracy (precision) value was between 0.2~0.4. (4) different disease model is applied in UAV images, there are differences in the response threshold and detection result, edge segmentation also has significant difference. The results show that the linear response to -0.95 recall crack the highest recall the highest reticular crack at -0.99 in response to -0.9 response, potholes. The highest recall on detection of local adaptive threshold segmentation and regional application of minimum connected Go to the Luqiao disease domain division to extract edge, shape and other parameters, and the classical Canny algorithm, Sobel algorithm, the proposed method can accurately extract the bridge disease information. (5) through the programming of Luqiao disease detection system based on UAV images, in the system design, function realization and verification are described. The result shows that this algorithm is effective and reliable, the system function is good, has a good application prospect.

【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U446

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