基于跑道邊界跟蹤與圖像融合的視景增強(qiáng)研究
發(fā)布時(shí)間：2018-10-25 14:36
【摘要】：針對(duì)雨、雪、霧等惡劣天氣導(dǎo)致的低能見(jiàn)度對(duì)飛機(jī)著陸安全性的不利影響,開(kāi)展了飛行員著陸視覺(jué)增強(qiáng)相關(guān)研究工作。通過(guò)跑道檢測(cè)、跟蹤與多源傳感器信息融合技術(shù)進(jìn)行面向飛行員著陸的視覺(jué)增強(qiáng)研究,以期顯著提高低能見(jiàn)度下機(jī)場(chǎng)跑道環(huán)境的可視性。本課題提出“基于跑道邊界跟蹤與圖像融合的視景增強(qiáng)算法”,該方法能夠有效利用機(jī)載前視紅外與可見(jiàn)光視頻圖像信息,顯著提高低能見(jiàn)度條件下飛行員的視覺(jué)感知能力。算法的主要優(yōu)點(diǎn)是:能夠更直觀的向飛行員指示跑道邊界等信息;通過(guò)圖像融合能夠綜合異源傳感器的互補(bǔ)信息,使視景增強(qiáng)效果更接近飛行員日常視景;本課題提出的視景增強(qiáng)算法具有較高的實(shí)時(shí)性。本文主要工作由以下部分組成:首先,分析了視景增強(qiáng)(EVS,Enhanced Vision System)技術(shù)的出現(xiàn)背景,并對(duì)視景增強(qiáng)技術(shù)的的理論研究與應(yīng)用研究現(xiàn)狀就行了介紹,對(duì)現(xiàn)在主要研究成果進(jìn)行了梳理。指明了現(xiàn)有視景增強(qiáng)技術(shù)的不足,并介紹了本文所提算法。其次,研究了機(jī)載前視紅外(FLIR)視頻首幀圖像的跑道檢測(cè)問(wèn)題。針對(duì)現(xiàn)有基于單特征的跑道檢測(cè)算法誤檢率高的問(wèn)題,提出了基于多特征融合的跑道檢測(cè)算法。該算法融合了直線、滅點(diǎn)、先驗(yàn)知識(shí)等信息聯(lián)合進(jìn)行跑道檢測(cè),極大地降低了誤檢率,提高了檢測(cè)精度。其中研究了LSD(Line Segment Detector)直線檢測(cè)、基于Gabor特征的滅點(diǎn)檢測(cè)、基于直線特征的滅點(diǎn)檢測(cè)等算法。然后,研究了機(jī)載前視紅外(FLIR)視頻序列中機(jī)場(chǎng)跑道的跟蹤問(wèn)題。針對(duì)跑道區(qū)域的大跨度特性和明顯的邊界約束性,提出了基于多采樣點(diǎn)聯(lián)合定位的跑道跟蹤算法。該算法充分利用跑道區(qū)域的稀疏特性,即跑道區(qū)域由跑道的邊界直線與圖像邊界共同決定,而直線的跟蹤又可以由直線上任意兩點(diǎn)的跟蹤實(shí)現(xiàn)。故而,通過(guò)對(duì)四個(gè)采樣點(diǎn)的跟蹤便實(shí)現(xiàn)了對(duì)整個(gè)跑道區(qū)域的精確定位。實(shí)驗(yàn)表明該算法具有較高的實(shí)時(shí)性與跟蹤精度。再者,研究了基于圖形學(xué)優(yōu)化理論的區(qū)域分割算法與基于多尺度分析的圖像融合算法。針對(duì)圖像多尺度分析只能針對(duì)矩形區(qū)域的特點(diǎn),對(duì)任意多邊形跑道區(qū)域進(jìn)行矩形化分割。采用迭代的方式對(duì)多邊形區(qū)域進(jìn)行分割直到達(dá)到最小閾值。其后,采用小波變換(WT,Wavelet Transform)對(duì)紅外與可見(jiàn)光視頻圖像的跑道區(qū)域(ROI,region of interest)進(jìn)行融合,而對(duì)跑道以外的區(qū)域采用簡(jiǎn)單的加權(quán)策略進(jìn)行融合。這樣就在保證跑道區(qū)域融合性能的同時(shí),滿足了視景增強(qiáng)實(shí)時(shí)性的要求。最后,在完成視景增強(qiáng)系統(tǒng)設(shè)計(jì)的基礎(chǔ)上,研究了基于邊緣特征的障礙物尺寸估計(jì)問(wèn)題。在飛機(jī)著陸過(guò)程中跑道中的微小障礙物都將對(duì)飛機(jī)安全構(gòu)成極大威脅,障礙物往往在可見(jiàn)光圖像中不易察覺(jué),而在紅外成像中會(huì)具有明顯的輪廓特征。算法首先對(duì)候檢區(qū)域進(jìn)行顯著性(Salience)檢測(cè),其后對(duì)顯著目標(biāo)進(jìn)行基于邊緣特征的尺寸估計(jì)。
[Abstract]:Aiming at the adverse effects of poor visibility caused by severe weather such as rain, snow and fog on the landing safety of aircraft, the related research work on visual enhancement of pilot landing was carried out. Based on runway detection tracking and multi-source sensor information fusion the visual enhancement for pilot landing is studied in order to improve the visibility of runway environment in low visibility. In this paper, a visual enhancement algorithm based on runway boundary tracking and image fusion is proposed. This method can effectively utilize airborne infrared and visible image information, and improve the visual perception ability of pilots under low visibility. The main advantages of the algorithm are that it can direct the runway boundary to the pilot more intuitively, and the complementary information of the heterogeneous sensor can be synthesized by image fusion, so that the visual enhancement effect is closer to the daily scene of the pilot. The scene enhancement algorithm proposed in this paper has high real-time performance. The main work of this paper consists of the following parts: firstly, the background of scene enhancement (EVS,Enhanced Vision System) technology is analyzed, and the present situation of theoretical research and application research of visual enhancement technology is introduced, and the main research results are summarized. The shortcomings of the existing scene enhancement techniques are pointed out, and the algorithms proposed in this paper are introduced. Secondly, the runway detection problem of the first frame image of airborne forward looking infrared (FLIR) video is studied. Aiming at the problem of high false detection rate of the existing runway detection algorithm based on single feature, a runway detection algorithm based on multi-feature fusion is proposed. The algorithm combines the information of straight line, vanishing point and prior knowledge for runway detection, which greatly reduces the false detection rate and improves the detection accuracy. The algorithms of LSD (Line Segment Detector) line detection, vanishing point detection based on Gabor feature and vanishing point detection based on line feature are studied. Then, the tracking problem of airport runway in airborne forward-looking infrared (FLIR) video sequence is studied. In view of the large span characteristic and obvious boundary constraint of runway region, a runway tracking algorithm based on joint location of multiple sampling points is proposed. The algorithm makes full use of the sparse property of the runway region, that is, the runway region is determined by the boundary line of the runway and the image boundary, and the tracking of the straight line can be realized by the tracking of any two points on the line. Therefore, by tracking the four sampling points, the accurate location of the entire runway area is realized. Experiments show that the algorithm has high real-time and tracking accuracy. Thirdly, the region segmentation algorithm based on graphic optimization theory and the image fusion algorithm based on multi-scale analysis are studied. In view of the multi-scale analysis of the image, only the characteristics of the rectangular region can be used to segment the arbitrary polygonal runway region. The polygon region is segmented by iterative method until the minimum threshold is reached. Subsequently, wavelet transform (WT,Wavelet Transform) is used to fuse the runway region (ROI,region of interest) of infrared and visible video images, while the region outside the runway is fused by a simple weighted strategy. In this way, the performance of runway area fusion is guaranteed, and the requirement of real-time scene enhancement is satisfied. Finally, based on the design of scene enhancement system, the problem of obstacle size estimation based on edge features is studied. The small obstacles in the runway will pose a great threat to the safety of the aircraft during the landing process. The obstacles are often difficult to detect in the visible image and have obvious contour characteristics in the infrared imaging. The algorithm firstly detects the significant (Salience) of the waiting area, and then estimates the size of the salient target based on the edge feature.
【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：V241;TP391.41
本文編號(hào)：2293984
[Abstract]:Aiming at the adverse effects of poor visibility caused by severe weather such as rain, snow and fog on the landing safety of aircraft, the related research work on visual enhancement of pilot landing was carried out. Based on runway detection tracking and multi-source sensor information fusion the visual enhancement for pilot landing is studied in order to improve the visibility of runway environment in low visibility. In this paper, a visual enhancement algorithm based on runway boundary tracking and image fusion is proposed. This method can effectively utilize airborne infrared and visible image information, and improve the visual perception ability of pilots under low visibility. The main advantages of the algorithm are that it can direct the runway boundary to the pilot more intuitively, and the complementary information of the heterogeneous sensor can be synthesized by image fusion, so that the visual enhancement effect is closer to the daily scene of the pilot. The scene enhancement algorithm proposed in this paper has high real-time performance. The main work of this paper consists of the following parts: firstly, the background of scene enhancement (EVS,Enhanced Vision System) technology is analyzed, and the present situation of theoretical research and application research of visual enhancement technology is introduced, and the main research results are summarized. The shortcomings of the existing scene enhancement techniques are pointed out, and the algorithms proposed in this paper are introduced. Secondly, the runway detection problem of the first frame image of airborne forward looking infrared (FLIR) video is studied. Aiming at the problem of high false detection rate of the existing runway detection algorithm based on single feature, a runway detection algorithm based on multi-feature fusion is proposed. The algorithm combines the information of straight line, vanishing point and prior knowledge for runway detection, which greatly reduces the false detection rate and improves the detection accuracy. The algorithms of LSD (Line Segment Detector) line detection, vanishing point detection based on Gabor feature and vanishing point detection based on line feature are studied. Then, the tracking problem of airport runway in airborne forward-looking infrared (FLIR) video sequence is studied. In view of the large span characteristic and obvious boundary constraint of runway region, a runway tracking algorithm based on joint location of multiple sampling points is proposed. The algorithm makes full use of the sparse property of the runway region, that is, the runway region is determined by the boundary line of the runway and the image boundary, and the tracking of the straight line can be realized by the tracking of any two points on the line. Therefore, by tracking the four sampling points, the accurate location of the entire runway area is realized. Experiments show that the algorithm has high real-time and tracking accuracy. Thirdly, the region segmentation algorithm based on graphic optimization theory and the image fusion algorithm based on multi-scale analysis are studied. In view of the multi-scale analysis of the image, only the characteristics of the rectangular region can be used to segment the arbitrary polygonal runway region. The polygon region is segmented by iterative method until the minimum threshold is reached. Subsequently, wavelet transform (WT,Wavelet Transform) is used to fuse the runway region (ROI,region of interest) of infrared and visible video images, while the region outside the runway is fused by a simple weighted strategy. In this way, the performance of runway area fusion is guaranteed, and the requirement of real-time scene enhancement is satisfied. Finally, based on the design of scene enhancement system, the problem of obstacle size estimation based on edge features is studied. The small obstacles in the runway will pose a great threat to the safety of the aircraft during the landing process. The obstacles are often difficult to detect in the visible image and have obvious contour characteristics in the infrared imaging. The algorithm firstly detects the significant (Salience) of the waiting area, and then estimates the size of the salient target based on the edge feature.
【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：V241;TP391.41
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