本文選題：目標檢測 + 水面目標　； 參考：《華中科技大學(xué)》2016年碩士論文

【摘要】：最近幾年,海洋經(jīng)濟總量持續(xù)快速增長,并成為拉動國民經(jīng)濟發(fā)展的重要引擎。海洋裝備和港口設(shè)施對海洋經(jīng)濟的發(fā)展起著重要的促進作用。無人艇作為一種智能型海洋裝備受到全世界的關(guān)注,并得到快速發(fā)展。本文重點關(guān)注水面目標的檢測與識別算法研究,在實際應(yīng)用中包括兩個方面:基于無人艇的水面目標檢測與識別算法研究和紅外圖像下泊港艦船檢測算法研究。針對基于無人艇的水面目標檢測與識別算法研究,由于水面氣候條件復(fù)雜、背景復(fù)雜多變、目標類型眾多,如果直接在原始圖上進行檢測,不僅難度較高,而且速度和準確率都很難得到保證。本文中提出了兩種不同的解決思路,一種是通過將目標性和顯著性相結(jié)合起來,首先,通過目標性分析得到目標候選區(qū)域,此時目標候選區(qū)域中會存在一定虛警信息,然后利用顯著性分析得到顯著性區(qū)域,最后將目標性與顯著性相結(jié)合,剔除虛警,得到目標準確位置,該算法不帶有特定目標類型信息,因此普適性較好;另外一種是將深度學(xué)習(xí)應(yīng)用到水面目標的檢測與識別中,并同時給出目標的具體類別信息和置信度。相比于現(xiàn)有的目標檢測與識別算法,本文中提出的算法無論準確率上還是在速度方面上都有一定的提升,對無人艇的自動避障和自主航行具有重要的指導(dǎo)意義。針對紅外圖像下泊港艦船檢測算法研究,由于紅外成像的特點,想要通過泊港內(nèi)艦船的邊緣和紋理來直接做檢測是非常困難的,并且實際場景中泊港內(nèi)的背景十分復(fù)雜,有艦船、港口、海水、道路等多種目標類型,同時艦船的姿態(tài)、位置、大小、數(shù)量都是不確定的�？紤]到以上幾個難點,本文通過兩步來實現(xiàn)泊港內(nèi)艦船檢測:港口區(qū)域檢測和艦船檢測,首先通過模板匹配將港口區(qū)域檢測出來,然后再在港口區(qū)域內(nèi)檢測艦船。這樣不僅可以縮小搜索的空間,提高算法的效率,還可以減少大量復(fù)雜的背景對算法的干擾,提高算法的準確率。最終通過實驗證明,本文提出的算法不僅準確率比較高,同時虛警率還比較低。
[Abstract]:In recent years, the total amount of marine economy has continued to grow rapidly, and has become an important engine for the development of the national economy. Marine equipment and port facilities play an important role in promoting the development of marine economy. As an intelligent marine equipment, unmanned craft (UAV) has attracted worldwide attention and developed rapidly. This paper focuses on the research of the detection and recognition algorithm of the surface target, including two aspects in the practical application: the research of the detection and recognition algorithm of the surface target based on the unmanned craft and the research of the detection algorithm of the ship in the port under the infrared image. For the research of surface target detection and recognition algorithm based on unmanned craft, it is not only difficult to detect the surface target directly on the original map, but also because of the complex climatic conditions, complex background and many target types. And speed and accuracy are difficult to guarantee. In this paper, two different solutions are proposed. One is to combine the goal and salience. Firstly, the target candidate region is obtained by the target analysis, and there will be some false alarm information in the target candidate region. Then the significance region is obtained by significance analysis. Finally, the target and significance are combined to eliminate false alarm and get the accurate position of the target. The algorithm does not contain the information of specific target type, so it is more general. The other is to apply depth learning to the detection and recognition of surface targets, and to give the specific information and confidence of the target at the same time. Compared with the existing target detection and recognition algorithms, the algorithm proposed in this paper has a certain improvement in both accuracy and speed, which has an important guiding significance for automatic obstacle avoidance and autonomous navigation of unmanned craft. Aiming at the research of ship detection algorithm in port under infrared image, because of the characteristics of infrared imaging, it is very difficult to detect the ship directly through the edges and textures of the ship in the port, and the background in the actual scene is very complex. There are many target types, such as ship, port, sea water, road and so on. At the same time, the attitude, position, size and number of ships are uncertain. Considering the above difficulties, this paper realizes the ship detection in the port through two steps: the port area detection and the ship detection. Firstly, the port area is detected by template matching, and then the ship is detected in the port area. This can not only reduce the search space, improve the efficiency of the algorithm, but also reduce a large number of complex background interference to the algorithm, improve the accuracy of the algorithm. Finally, the experimental results show that the proposed algorithm not only has high accuracy, but also has a low false alarm rate.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U675.79;TP391.41

【參考文獻】

相關(guān)期刊論文 前6條

1 戴華東;胡謀法;盧煥章;王陽;;基于連通域標記的目標檢測算法設(shè)計與實現(xiàn)[J];現(xiàn)代電子技術(shù);2015年20期

2 惠國保;童一飛;李東波;;基于改進的圖像局部區(qū)域相似度學(xué)習(xí)架構(gòu)的圖像特征匹配技術(shù)研究[J];計算機學(xué)報;2015年06期

3 王凱;黃山;趙瑜;張洪斌;;面向圖像目標提取的改進連通域標記算法[J];計算機工程與設(shè)計;2014年07期

4 楊建龍;;我國海洋經(jīng)濟發(fā)展的現(xiàn)狀、問題與建議[J];發(fā)展研究;2014年01期

5 李家良;;水面無人艇發(fā)展與應(yīng)用[J];火力與指揮控制;2012年06期

6 王千;王成;馮振元;葉金鳳;;K-means聚類算法研究綜述[J];電子設(shè)計工程;2012年07期

，

本文編號：1821037