本文選題：交通信息工程　切入點(diǎn)：智能車　出處：《交通運(yùn)輸工程學(xué)報》2017年03期

【摘要】：為了提高交通標(biāo)志識別的正確率和實(shí)時性,提出了一種基于HOG-Gabor特征融合與Softmax分類器的交通標(biāo)志識別方法。采用Gamma矯正方法提取HOG特征,采用對比度受限的自適應(yīng)直方圖均衡化方法提取Gabor特征,基于線性特征融合原理,將提取的HOG和Gabor特征向量直接串聯(lián),得到刻畫交通標(biāo)志的融合特征向量,采用Softmax分類器對融合特征向量進(jìn)行分類,采用德國交通標(biāo)志識別基準(zhǔn)(GTSRB)數(shù)據(jù)庫測試了所提方法的有效性,比較了基于單特征與融合特征的交通標(biāo)志識別效果。試驗(yàn)結(jié)果表明:在圖像增強(qiáng)過程中,針對HOG特征,采用Gamma矯正方法的分類正確率最大,為97.11%,針對Gabor特征,采用限制對比度的直方圖均衡化方法的分類正確率最大,為97.54%;采用Softmax分類器的最小分類正確率為97.11%,耗時小于2s;針對HOG-Gabor融合特征,采Softmax分類器的識別率高達(dá)97.68%,因此,基于HOG-Gabor特征融合與Softmax分類器的交通標(biāo)志識別方法的識別率高,實(shí)時性強(qiáng)。
[Abstract]:In order to improve the accuracy and real time of traffic sign recognition, a traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier is proposed. Gamma correction method is used to extract HOG features. The adaptive histogram equalization method with limited contrast is used to extract Gabor features. Based on the principle of linear feature fusion, the extracted HOG and Gabor feature vectors are connected in series directly, and the fused feature vectors depicting traffic signs are obtained. The fusion feature vector is classified by Softmax classifier, and the validity of the proposed method is tested by using the German Traffic sign recognition benchmark (GTSRB) database. The effect of traffic sign recognition based on single feature and fusion feature is compared. The experimental results show that in the process of image enhancement, the correct rate of Gamma correction method is 97.11 for HOG feature, and 97.11 for Gabor feature. The histogram equalization method with restricted contrast has the highest classification accuracy of 97.54; the minimum classification accuracy with Softmax classifier is 97.11 and the time consuming is less than 2 s; for the HOG-Gabor fusion feature, the recognition rate of the Softmax classifier is as high as 97.68, so the recognition rate of the Softmax classifier is as high as 97.68. The traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier has high recognition rate and high real-time performance.
【作者單位】： 長安大學(xué)信息工程學(xué)院;廣東省特種設(shè)備檢測研究院珠海檢測院;神龍汽車有限公司;伊利諾伊大學(xué)芝加哥分校土木與材料工程系;
【基金】：國家自然科學(xué)基金項(xiàng)目(61201406) 中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目(310824162022)
【分類號】：TP391.41;U463.6
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本文編號：1666296