本文選題：深海海底圖像 + 飛機(jī)殘骸識別�。� 參考：《大連海事大學(xué)》2017年碩士論文

【摘要】：近年來,空難事故頻發(fā),與陸地上空難不同,飛機(jī)在海上發(fā)生空難時(shí)機(jī)身會(huì)摔成碎片并沉入海底,在深海海底對飛機(jī)殘骸碎片進(jìn)行探測對飛機(jī)黑匣子的打撈具有重要意義。搜尋水下目標(biāo)如同大海撈針,特別是在深海環(huán)境中,變得尤為困難。通常需要通過AUV搭載聲吶和水下相機(jī)進(jìn)行目標(biāo)搜索,并利用水下相機(jī)圖像進(jìn)行取證,最終由人眼進(jìn)行目標(biāo)確認(rèn)。由于AUV在水下工作時(shí)間較長,水下相機(jī)拍攝的圖像數(shù)量十分巨大,而包含飛機(jī)殘骸的圖像數(shù)量卻很少,如何濾除大量無效圖像,提高檢測的效率,是本文研究的核心問題。本文研究了深海海底背景和飛機(jī)殘骸的特性,提出了飛機(jī)殘骸檢測方法,該方法先檢測圖像中的疑似目標(biāo)區(qū)域,而后對疑似區(qū)域進(jìn)行判決。在疑似目標(biāo)區(qū)域檢測中,首先,針對飛機(jī)殘骸具有明顯的形狀和線條的特點(diǎn),利用Hough變換直線檢測算法來檢測圖像中的直線,并將檢測結(jié)果標(biāo)記在圖像中以增強(qiáng)有效邊緣,而后再利用基于圖論的圖像顯著性算法(Graph Based Visual Saliency,GBVS)獲取該圖像的顯著度圖,將顯著度最高的幾組區(qū)域標(biāo)記為目標(biāo)疑似區(qū)域。在確認(rèn)疑似目標(biāo)區(qū)域是否為飛機(jī)殘骸時(shí),采用支持向量機(jī)(Support Vector Machine,SVM)分類器,針對深海海底背景圖像特性和飛機(jī)殘骸圖像特性,提出了平均亮度、對比度、邊緣密度和紋理方差四個(gè)指標(biāo)作為支持向量機(jī)分類器的特征向量,并利用深海背景圖像和飛機(jī)殘骸圖像制作了訓(xùn)練圖像庫,訓(xùn)練了支持向量機(jī)分類器,利用該分類器可實(shí)現(xiàn)對目標(biāo)疑似區(qū)域的判決。為驗(yàn)證本文算法,開展了深水水池成像實(shí)驗(yàn)和近海海底成像實(shí)驗(yàn),盡量模擬深海的景物特點(diǎn)和工作環(huán)境,獲取了深海海底模擬圖像數(shù)據(jù),并對采集圖像進(jìn)行了目標(biāo)檢測,計(jì)算了漏警率,和有效圖像在總圖像中所占比率。實(shí)驗(yàn)檢測結(jié)果表明,本文提出的算法具有低漏警率的特點(diǎn),在保留有效圖像的同時(shí),可以大量濾除無效圖像,從而大幅降低需人工判讀的圖像數(shù)量。
[Abstract]:In recent years, there are frequent air accidents, which are different from those on land. The fuselage will fall into pieces and sink to the bottom of the sea when there is an air accident on the sea. It is of great significance to detect the debris of aircraft wreckage in the deep sea and salvage the black box of the aircraft. Searching for underwater targets is like looking for a needle in a haystack, especially in deep-sea environments. Usually, it is necessary to carry out target search by AUV sonar and underwater camera, and use underwater camera image to obtain evidence, and finally confirm the target by human eyes. Because of the long working time of AUV under water, the number of images taken by underwater camera is very large, but the number of images containing airplane wreckage is very small. How to filter a large number of invalid images and improve the efficiency of detection is the core problem of this paper. In this paper, the background of deep sea and the characteristics of aircraft wreckage are studied, and a detection method of aircraft wreckage is proposed. The method first detects the suspected target area in the image, and then judges the suspected area. In the detection of the suspected target area, firstly, aiming at the obvious shape and line characteristics of the wreckage, the Hough transform line detection algorithm is used to detect the straight line in the image, and the detection result is labeled in the image to enhance the effective edge. Then, the graph Based Visual salience graph is used to obtain the salience graph of the image, and some regions with the highest saliency are marked as suspected regions. When confirming whether the suspected target area is the wreckage of the aircraft, the support vector machine support Vector Machine (SVM) classifier is used to propose the average brightness and contrast for the background image characteristics of the deep sea floor and the image characteristics of the wreckage. The edge density and texture variance are used as the feature vectors of SVM classifier, and the training image database is made by using deep-sea background image and airplane wreckage image, and the SVM classifier is trained. By using this classifier, the target suspected area can be judged. In order to verify this algorithm, the deep water pool imaging experiment and the offshore seabed imaging experiment are carried out to simulate the scene characteristics and working environment of the deep sea as far as possible, and obtain the deep sea bottom simulation image data, and carry on the target detection to the collected image. The false alarm rate and the ratio of the effective image to the total image are calculated. The experimental results show that the proposed algorithm has the characteristics of low false alarm rate and can filter out invalid images at the same time of preserving effective images, thus greatly reducing the number of images that need manual interpretation.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V328;TP391.41

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