【摘要】：紅外成像技術(shù)在軍事目標(biāo)檢測與識別中應(yīng)用越來越廣泛�；谇耙暭t外成像的探雷技術(shù)具有探測成像分辨率高、視場范圍大、效率高、精度高等優(yōu)點(diǎn),已成為世界各國成像探雷技術(shù)研究的熱點(diǎn)。本文以地面拋撒地雷探測為目標(biāo),按照系統(tǒng)設(shè)計與實(shí)現(xiàn)的流程,對紅外成像技術(shù)在探雷系統(tǒng)中的應(yīng)用進(jìn)行分析,主要從如下幾個方面展開研究工作:(1)從紅外成像技術(shù)應(yīng)用于探雷系統(tǒng)的基本原理出發(fā),進(jìn)行可行性分析;然后結(jié)合地面拋撒地雷特征,通過紅外輻射數(shù)學(xué)建模實(shí)現(xiàn)不同環(huán)境條件的影響因素分析,并給出幾種特殊的影響因素。(2)搭建基于車載的紅外成像探雷系統(tǒng),從系統(tǒng)組成、基本工作原理以及系統(tǒng)工作流程進(jìn)行分析,并設(shè)計算法處理流程實(shí)現(xiàn)地雷目標(biāo)的檢測、識別及定位。(3)根據(jù)紅外成像焦平面陣列可能存在的非均勻性噪聲,選取列均衡化的非均勻性校正算法,復(fù)原降質(zhì)的紅外圖像;再運(yùn)用自適應(yīng)分段線性變換算法,達(dá)到增強(qiáng)地雷目標(biāo)抑制復(fù)雜背景的目的,且在處理效率上滿足系統(tǒng)實(shí)時性要求。(4)結(jié)合預(yù)處理后的紅外地雷圖像,從基于閾值的分割方法出發(fā),根據(jù)紅外布雷場景灰度分布特征,選取交叉熵約束的閾值分割方法,與傳統(tǒng)的類間方差或最大熵方法相比,分割的地雷目標(biāo)更準(zhǔn)確;為了后期地雷目標(biāo)的精確識別,還對閾值分割后的圖像進(jìn)行背景噪聲消除和地雷目標(biāo)空洞填充。(5)利用紅外布雷場景分割后的二值圖像,統(tǒng)計不同待識別地雷目標(biāo)的連通域;根據(jù)特征不變量的紅外圖像目標(biāo)識別原理,建立似圓形地雷目標(biāo)的凸殼不變量模型,求解待識別目標(biāo)區(qū)域的凸殼不變量,并進(jìn)行匹配與篩選;最后利用灰度重心定位算法實(shí)現(xiàn)地雷目標(biāo)位置坐標(biāo)的標(biāo)識。實(shí)驗(yàn)結(jié)果表明,本文選取的算法處理流程不僅能夠?qū)崿F(xiàn)紅外布雷場景中地雷目標(biāo)的高精度探測,而且滿足紅外成像探雷系統(tǒng)實(shí)時性處理要求。
[Abstract]:Infrared imaging technology is more and more widely used in military target detection and recognition. Mine detection technology based on forward-looking infrared imaging has the advantages of high resolution, wide field of view, high efficiency and high precision. It has become a hot spot in the research of imaging mine detection technology all over the world. According to the flow of system design and realization, the application of infrared imaging technology in mine detection system is analyzed in this paper. Mainly from the following aspects of research work: (1) from the infrared imaging technology applied to the basic principles of mine detection system, the feasibility analysis; Then combined with the characteristics of ground-thrown mines, the influence factors of different environmental conditions are analyzed by mathematical modeling of infrared radiation, and several special factors are given. (2) the infrared imaging mine detection system based on vehicle is built, which is composed of the system. The basic working principle and the workflow of the system are analyzed, and the algorithm processing flow is designed to realize the detection, recognition and location of mine targets. (3) according to the possible non-uniform noise of infrared imaging focal plane array, The non-uniformity correction algorithm of column equalization is selected to recover the degraded infrared image. Then the adaptive piecewise linear transformation algorithm is used to enhance the mine target suppression complex background, and the processing efficiency meets the real-time requirements of the system. (4) combined with the pre-processed infrared mine image, Starting from the threshold-based segmentation method, according to the gray distribution characteristics of infrared mine-laying scene, the threshold segmentation method with cross-entropy constraint is selected. Compared with the traditional inter-class variance or maximum entropy method, the mine target segmentation is more accurate. In order to accurately identify the mine target in the later stage, the background noise and the hole filling of the mine target are removed from the threshold segmented image. (5) the binary image of the infrared mine-laying scene is used to segment the image. (B) Statistics the connectivity areas of different mine targets to be identified; According to the principle of infrared image target recognition with feature invariants, the convex hull invariants model of similar circular mine targets is established, and the convex hull invariants in the target region to be identified are solved, and the matching and screening are carried out. Finally, the gray center of gravity localization algorithm is used to mark the location coordinates of mine targets. The experimental results show that the proposed algorithm can not only realize the high precision detection of mine targets in the infrared mine-laying scene, but also meet the real-time processing requirements of infrared imaging mine detection system.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41;TN219

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