本文選題：車輛交通圖像　切入點(diǎn)：光照特征　出處：《計(jì)算機(jī)工程與應(yīng)用》2017年24期 　論文類型：期刊論文

【摘要】：為了準(zhǔn)確檢測(cè)出車輛交通圖像的光照類型,從而有針對(duì)性地矯正不同光照以減少其對(duì)車牌定位的影響,提出了一種基于改進(jìn)K近鄰和支持向量相融合(KNN-SVM)的車輛圖像光照檢測(cè)方法。首先融合了HSV空間亮度特征、灰度直方圖特征和投影直方圖特征作為車輛圖像的光照特征,然后改進(jìn)傳統(tǒng)KNN-SVM中距離計(jì)算方法,定義為每類待檢測(cè)樣本到屬于該類支持向量的距離,并在采集的全天候不同光照車輛圖像上進(jìn)行檢測(cè)驗(yàn)證。實(shí)驗(yàn)表明,改進(jìn)KNNSVM將閾值獲取時(shí)間提前,避免了傳統(tǒng)KNN-SVM對(duì)超平面附近樣本先SVM檢測(cè)再KNN檢測(cè)的重復(fù)檢測(cè),不僅降低了算法復(fù)雜度和運(yùn)行時(shí)間,且檢測(cè)準(zhǔn)確率高于傳統(tǒng)KNN-SVM和單獨(dú)使用KNN或SVM時(shí)的值,最高達(dá)到了99.67%。
[Abstract]:In order to detect the illumination type of vehicle traffic image accurately, and correct different illumination in order to reduce its influence on license plate location, A vehicle image illumination detection method based on improved K-nearest neighbor and support vector fusion (KNN-SVM) is proposed. Firstly, the luminance feature of HSV space, gray histogram feature and projection histogram feature are combined as illumination features of vehicle image. Then, the distance calculation method in traditional KNN-SVM is improved, which is defined as the distance between each kind of samples to be detected and the support vector of this class, and the detection verification is carried out on the collected all-weather and different illumination vehicle images. The experimental results show that, The improved KNNSVM improves the threshold acquisition time in advance, avoids the repeated detection of the traditional KNN-SVM to samples near the hyperplane by first SVM detection and KNN detection, which not only reduces the algorithm complexity and running time, but also reduces the algorithm complexity and running time. The accuracy of detection is higher than that of traditional KNN-SVM and using KNN or SVM alone, and the highest is 99.67.
【作者單位】： 北京信息科技大學(xué)計(jì)算機(jī)學(xué)院計(jì)算機(jī)系統(tǒng)開放實(shí)驗(yàn)室;
【基金】：寧波市鎮(zhèn)海區(qū)2016年引進(jìn)高層次人才創(chuàng)業(yè)項(xiàng)目
【分類號(hào)】：TP18;TP391.41

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本文編號(hào)：1609596