發(fā)布時(shí)間：2018-09-10 14:55

【摘要】：隨著技術(shù)的進(jìn)步,近年來(lái)無(wú)人機(jī)在架空輸電線(xiàn)路巡檢中占據(jù)了非常重要的地位。國(guó)內(nèi)外圍繞無(wú)人機(jī)拍攝影像的自動(dòng)檢測(cè)處理展開(kāi)了大量的研究和應(yīng)用,但是巡檢時(shí)輸電線(xiàn)目標(biāo)極容易脫離鏡頭畫(huà)面,造成漏檢問(wèn)題。為了使無(wú)人機(jī)巡檢更具有實(shí)用性,影像的智能采集是研究的關(guān)鍵,必須解決無(wú)人機(jī)的長(zhǎng)距離自主巡航以及巡航過(guò)程中機(jī)載相機(jī)準(zhǔn)確跟隨線(xiàn)路拍攝等問(wèn)題。本文針對(duì)這一問(wèn)題提出了一種基于空間視覺(jué)檢測(cè)的新型巡檢方案,依托衛(wèi)星導(dǎo)航的長(zhǎng)航時(shí)多旋翼無(wú)人機(jī)搭載紅外相機(jī)和可見(jiàn)光相機(jī)同時(shí)拍攝線(xiàn)路,紅外相機(jī)則負(fù)責(zé)跟蹤輸電線(xiàn)路,通過(guò)對(duì)線(xiàn)路特征較清晰的紅外圖像進(jìn)行分析處理,輸出控制信號(hào)及時(shí)調(diào)整云臺(tái)的角度,保證輸電線(xiàn)目標(biāo)不會(huì)移出鏡頭畫(huà)面�？梢�(jiàn)光相機(jī)同步拍攝線(xiàn)路、金具、絕緣子等細(xì)節(jié)照片,達(dá)到高質(zhì)量巡檢的目的。文中采用權(quán)值窗口優(yōu)化的光流法進(jìn)行紅外圖像處理,特別研究了桿塔轉(zhuǎn)角時(shí)出現(xiàn)的短暫不等距斜視問(wèn)題,提出了基于桿塔標(biāo)志校正的方法,且對(duì)所涉及算法進(jìn)行了分析與實(shí)驗(yàn)驗(yàn)證。提出了一種基于k-b參數(shù)空間的模板集合匹配對(duì)直線(xiàn)檢測(cè)和擬合的圖像處理算法,提高了對(duì)線(xiàn)路和桿塔的提取清晰度。完成了云臺(tái)角度調(diào)整的理論驗(yàn)證。本文針對(duì)上述問(wèn)題搭建了硬件實(shí)驗(yàn)平臺(tái),制作八旋翼無(wú)人機(jī)、可見(jiàn)光相機(jī)、紅外相機(jī)以及云臺(tái)、飛控等設(shè)備,進(jìn)行了相關(guān)的理論驗(yàn)證和實(shí)驗(yàn)分析。通過(guò)仿真實(shí)驗(yàn)發(fā)現(xiàn),上述方法可以有效的保證無(wú)人機(jī)搭載相機(jī)對(duì)輸電線(xiàn)巡檢圖像的采集,在沒(méi)有不可控因素出現(xiàn)的條件下,能夠很好捕獲輸電線(xiàn)和桿塔,且計(jì)算有效的云臺(tái)調(diào)整參數(shù),對(duì)輸電線(xiàn)路實(shí)現(xiàn)較好的跟蹤,從而為無(wú)人機(jī)視覺(jué)追蹤巡檢提供一定理論參考。
[Abstract]:With the development of technology, UAV plays an important role in overhead transmission line inspection in recent years. A large number of researches and applications have been carried out around the automatic detection and processing of UAV images at home and abroad, but transmission line targets are very easy to detach from the lens picture during patrol inspection, which results in the problem of missing detection. In order to make the UAV patrol more practical, the intelligent image acquisition is the key to the research. It is necessary to solve the long distance autonomous cruise of UAV and the accurate track shooting of the airborne camera during the cruise. In order to solve this problem, a new inspection scheme based on space vision detection is proposed in this paper. The multi-rotor UAV based on satellite navigation is equipped with infrared camera and visible light camera to shoot the line simultaneously. The infrared camera is responsible for tracking the transmission lines. By analyzing and processing the infrared images with clear line features, the output control signal adjusts the angle of the cloud head in time, so as to ensure that the transmission line targets will not move out of the lens. Visible-light camera synchronously takes details such as lines, hardware, insulators and so on to achieve high-quality inspection. In this paper, the weight window optimized optical flow method is used for infrared image processing, especially the problem of transient non-isometric squint in the rotation angle of the tower is studied, and a method based on pole and tower marking correction is proposed. The algorithm is analyzed and verified by experiments. An image processing algorithm based on k-b parameter space is proposed to detect and fit straight lines by template set matching, which improves the clarity of line and tower extraction. The theoretical verification of the angle adjustment of the cloud head is completed. In order to solve the above problems, this paper builds a hardware experimental platform, and makes eight rotary-wing UAV, visible light camera, infrared camera, cloud head, flight control and other equipment, and carries out relevant theoretical verification and experimental analysis. Through simulation experiments, it is found that the above method can effectively ensure that the UAV can carry the camera to collect the image of the transmission line patrol inspection, and can capture the transmission line and tower very well without any uncontrollable factors. The effective parameters of cloud head adjustment are calculated, and the transmission line is tracked well, which provides a certain theoretical reference for the visual tracking inspection of UAV.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM755

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 丁健;傅寒凝;唐勁飛;程宏斌;;基于無(wú)人機(jī)中繼的電力巡檢數(shù)據(jù)傳輸方案設(shè)計(jì)[J];空軍預(yù)警學(xué)院學(xué)報(bào);2016年06期

2 孫挺;齊迎春;耿國(guó)華;;基于幀間差分和背景差分的運(yùn)動(dòng)目標(biāo)檢測(cè)算法[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2016年04期

3 魏文力;丁建;黃建峰;;固定翼無(wú)人機(jī)電力巡線(xiàn)航線(xiàn)規(guī)劃淺析[J];中國(guó)電業(yè)(技術(shù)版);2016年04期

4 黃建峰;姜云土;時(shí)滿(mǎn)宏;曹詩(shī)卉;;全真可視化虛擬無(wú)人機(jī)巡線(xiàn)控制技術(shù)研究[J];空軍預(yù)警學(xué)院學(xué)報(bào);2016年01期

5 彭向陽(yáng);陳馳;饒章權(quán);楊必勝;麥曉明;王柯;;基于無(wú)人機(jī)多傳感器數(shù)據(jù)采集的電力線(xiàn)路安全巡檢及智能診斷[J];高電壓技術(shù);2015年01期

6 汪國(guó)強(qiáng);蓋琪琳;于懷勇;文雪;任天威;;基于背景差分法的視頻目標(biāo)檢測(cè)算法研究[J];黑龍江大學(xué)工程學(xué)報(bào);2014年04期

7 王燕玲;李廣倫;林曉;;基于跟蹤特征的多運(yùn)動(dòng)目標(biāo)分類(lèi)算法[J];中國(guó)體視學(xué)與圖像分析;2014年02期

8 于坤林;謝志宇;原振文;;改進(jìn)的小波圖像融合算法及應(yīng)用研究[J];計(jì)算機(jī)與數(shù)字工程;2014年04期

9 屈晶晶;辛云宏;;連續(xù)幀間差分與背景差分相融合的運(yùn)動(dòng)目標(biāo)檢測(cè)方法[J];光子學(xué)報(bào);2014年07期

10 付昱瑋;李字明;姜洪;;無(wú)人機(jī)巡線(xiàn)的發(fā)展和應(yīng)用研究[J];黑龍江科技信息;2014年03期

相關(guān)博士學(xué)位論文 前3條

1 屈鑒銘;智能視頻監(jiān)控中的運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤技術(shù)研究[D];西安電子科技大學(xué);2015年

2 鄭維剛;基于無(wú)人機(jī)紅外影像技術(shù)的配電網(wǎng)巡檢系統(tǒng)研究[D];沈陽(yáng)農(nóng)業(yè)大學(xué);2014年

3 袁國(guó)武;智能視頻監(jiān)控中的運(yùn)動(dòng)目標(biāo)檢測(cè)和跟蹤算法研究[D];云南大學(xué);2012年

相關(guān)碩士學(xué)位論文 前2條

1 李力;無(wú)人機(jī)輸電線(xiàn)路巡線(xiàn)技術(shù)及其應(yīng)用研究[D];長(zhǎng)沙理工大學(xué);2012年

2 呂明;基于多傳感器協(xié)同檢測(cè)的輸電線(xiàn)路無(wú)人飛機(jī)巡檢關(guān)鍵技術(shù)[D];上海交通大學(xué);2012年

，

本文編號(hào)：2234779