本文選題：目標(biāo)檢測 + 艦船檢測��； 參考：《光學(xué)精密工程》2017年09期

【摘要】：為了精確地檢測到艦船目標(biāo),提出了一種基于多特征、多尺度視覺顯著性的海面艦船目標(biāo)檢測方法。該方法首先利用多尺度自適應(yīng)的頂帽算法抑制云層、油污的干擾,然后提取雙顏色空間特征以及邊緣特征構(gòu)成雙四元數(shù)圖像進(jìn)行艦船顯著性檢測。由于充分利用了雙四元數(shù)圖像,故可對多個特征尺度進(jìn)行處理,并保證不同尺度特征之間關(guān)聯(lián)性。該方法還利用人眼對不同用大小的圖像關(guān)注目標(biāo)不同的特點對圖像進(jìn)行上下采樣以避免漏檢和檢測重疊。在得到顯著圖后利用自適應(yīng)圖像分割(OTSU)算法確定艦船所在的區(qū)域,并在原圖上標(biāo)定、提取艦船目標(biāo)。在多種海面情況下進(jìn)行了實驗分析,結(jié)果表明:該算法可以排除多種干擾,精確地檢測到艦船目標(biāo),真正率達(dá)97.73%,虛警率低至3.37%,相較于他頻域顯著性檢測算法在艦船檢測方面有明顯的優(yōu)勢。
[Abstract]:In order to accurately detect ship targets, a multi-feature and multi-scale visual salience method is proposed for ship target detection on the sea surface.Firstly, the multi-scale adaptive top cap algorithm is used to suppress the interference of cloud and oil pollution, and then the binary quaternion images are extracted to detect ship salience.Because the binary quaternion images are fully utilized, many feature scales can be processed and the correlation between different scale features can be ensured.The method also makes use of the characteristics of human eyes to focus on different objects of different sizes to sample the images up and down in order to avoid missing detection and detection overlap.After getting salient images, adaptive image segmentation algorithm (OTSUA) is used to determine the region where the ship is located and calibrated on the original image to extract the ship target.The experimental results show that the algorithm can eliminate many kinds of disturbances and detect ship targets accurately.The real rate is 97.73 and the false alarm rate is as low as 3.37. Compared with his significant detection algorithm in frequency domain, it has obvious advantages in ship detection.
【作者單位】： 中國科學(xué)院長春光學(xué)精密機(jī)械與物理研究所中科院航空光學(xué)成像與測量重點實驗室;中國科學(xué)院大學(xué);
【基金】：吉林省重大科技攻關(guān)專項基金資助項目(No.11ZDGG001) 吉林省自然科學(xué)基金資助項目(No.20150101017JC)
【分類號】：TP751

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