發(fā)布時間：2019-06-05 05:47

【摘要】：紅外成像技術(shù)隱蔽性好、抗干擾能力強且能全天候工作,被廣泛應(yīng)用于民用和軍事領(lǐng)域。但紅外弱小目標的成像距離遠,背景雜波干擾嚴重,使得其成像信噪比低、結(jié)構(gòu)信息不足,對紅外圖像預(yù)處理方法、弱小目標檢測以及弱小目標跟蹤方法的研究成為紅外弱小多目標實時處理技術(shù)的關(guān)鍵,在紅外制導(dǎo)等領(lǐng)域起到關(guān)鍵作用。本論文以實際科研項目為研究背景,基于FPGA+DSP架構(gòu)實現(xiàn)紅外圖像的實時處理方法的設(shè)計與優(yōu)化。紅外弱小多目標圖像背景雜波和噪聲干擾嚴重,圖像預(yù)處理至關(guān)重要,于是本文采用改進中值濾波來適應(yīng)不同噪聲類型和噪聲密度,在改變滑動窗口長度的同時改變窗口形狀,并保證以較小的窗口進行濾波�；谀繕撕捅尘疤卣�,采用改進形態(tài)學(xué)背景抑制算法,通過尺度變化的半圈組合型結(jié)構(gòu)元提取起伏變化的圖像背景,實現(xiàn)背景雜波的有效抑制。在弱小多目標檢測方面,結(jié)合帶尺度因子的點擴散模型,在拉普拉斯高斯尺度空間下表示圖像,初步確定目標位置和大小,通過各向差異度均值的閾值判斷提取出所有可疑目標,再根據(jù)目標大小結(jié)合各向差異度實現(xiàn)真實目標的檢測。在弱小多目標的跟蹤方面,為了實現(xiàn)可靠跟蹤,匹配不同數(shù)據(jù)更新速率的濾波器,對低速運動目標匹配均值漂移卡爾曼濾波器,而高機動運動目標匹配改進均值漂移粒子濾波器,并進行交互式融合,得到目標跟蹤結(jié)果,而為了實現(xiàn)多目標的可靠跟蹤,通過結(jié)合馬爾可夫隨機網(wǎng)絡(luò),考慮了各個目標的相鄰目標的狀態(tài),估計出每個目標最大聯(lián)合后驗概率,更新濾波器參數(shù)和粒子權(quán)值,進行多目標位置估計。基于紅外實時圖像處理平臺,通過算法移植,對分辨率為640*512的中波紅外相機連續(xù)拍攝的大量數(shù)據(jù)進行實時處理,處理結(jié)果表明,在圖像預(yù)處理方面,本文采用的改進中值濾波噪聲平滑算法和改進形態(tài)學(xué)背景抑制算法具有較好的處理效果和實時性;在多目標的檢測方面,本文采用的基于尺度空間的各向差異度檢測算法相比已有算法,魯棒性更好,具有更高的檢測率,同時單幀的平均處理時間小于5ms,滿足處理實時性要求;在多目標的跟蹤方面,對于本文采用的結(jié)合馬爾可夫隨機網(wǎng)絡(luò)的多模型改進卡爾曼粒子濾波方法,其跟蹤正確率是傳統(tǒng)交互式多模型算法的3倍,處理速度能達到72幀/S,跟蹤可靠性高、實時性好。綜合而言,本文的紅外弱小多目標實時處理方法可靠,有很高的實際應(yīng)用價值。
[Abstract]:Infrared imaging technology is widely used in civil and military fields because of its good concealment, strong anti-interference ability and all-weather work. However, the imaging distance of infrared small and weak targets is long and the background clutter interference is serious, which makes the imaging signal-to-noise ratio (SNR) low and the structure information insufficient. The research of weak and small target detection and weak small target tracking method has become the key to infrared small and weak multi-target real-time processing technology, and plays a key role in infrared guidance and other fields. This paper takes the actual scientific research project as the research background, based on FPGA DSP architecture to realize the design and optimization of infrared image real-time processing method. The background clutter and noise interference of infrared weak and small multi-target image is serious, so image preprocessing is very important. Therefore, this paper adopts improved median filtering to adapt to different noise types and noise density, and changes the window shape while changing the length of sliding window. And make sure to filter with a smaller window. Based on the target and background characteristics, the improved morphological background suppression algorithm is adopted, and the fluctuating image background is extracted by the semi-circular combined structure element with scale change, and the background clutter is effectively suppressed. In the aspect of weak and small multi-target detection, combined with the point diffusion model with scale factor, the image is represented in Laplace Gao Si scale space, and the target position and size are preliminarily determined. All suspicious targets are extracted by the threshold judgment of the mean value of each direction difference, and then the real target detection is realized according to the target size combined with the difference degree of each direction. In the aspect of weak and small multi-target tracking, in order to realize reliable tracking and match the filter with different data update rate, the mean drift Kalman filter is matched to the low-speed moving target. The high maneuvering moving target matching improves the mean drift particle filter, and carries on the interactive fusion to obtain the target tracking result. In order to realize the reliable tracking of the multi-target, in order to realize the reliable tracking of the multi-target, by combining the Markov random network, Considering the state of the adjacent targets of each target, the maximum joint posterior probability of each target is estimated, the filter parameters and particle weights are updated, and the multi-target position estimation is carried out. Based on the infrared real-time image processing platform, a large number of data taken continuously by the medium-wave infrared camera with a resolution of 640 / 512 are processed in real time through the algorithm transplantation. The processing results show that in the aspect of image preprocessing, The improved median filter noise smoothing algorithm and the improved morphological background suppression algorithm used in this paper have good processing effect and real-time performance. In the aspect of multi-target detection, the proposed algorithm based on scale space has better robustness and higher detection rate than the existing algorithms, and the average processing time of a single frame is less than 5 Ms. Meet the real-time requirements of processing; In the aspect of multi-target tracking, the tracking accuracy of the multi-model improved Kalman particle filter combined with Markov stochastic network is three times higher than that of the traditional interactive multi-model algorithm, and the processing speed can reach 72 frames / S. The tracking reliability is high and the real-time performance is good. To sum up, the real-time processing method of infrared weak and small multi-target in this paper is reliable and has high practical application value.
【學(xué)位授予單位】：蘇州科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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