【摘要】：近年來,越來越多的研究者開始關注智能交通系統(tǒng)(ITS),而交通標志檢測是智能交通系統(tǒng)的重要環(huán)節(jié),并且是交通標志識別的前提,具有重要的研究意義和應用價值。面對相對復雜的自然場景,交通標志檢測目前尚沒有成熟的實際應用,因此需要繼續(xù)研究一種對于復雜場景下的交通標志普遍適用的檢測方案。本文針對復雜環(huán)境下的交通標志檢測算法進行研究,目的是提出一種對實際應用時的各種場景均適用的檢測方案。本文的主要工作有如下幾個方面:(1)對比分析幾種基于顏色空間的交通標志分割方法,針對固定閾值顏色分割方法的缺陷提出一種基于RGB顏色空間的自適應閾值分割方法RGBAT(RGB Color Space Adaptive Threshold)。該方法能有效克服固定閾值分割方法易受光照、標志褪色、陰影等因素影響的缺陷。通過與幾種基于顏色空間的交通標志分割方法進行對比實驗分析驗證了RGBAT方法的有效性。(2)依據(jù)交通標志檢測應用對梯度直方圖特征HOG與HSV量化直方圖特征進行簡化,在保證特征的分類能力的同時降低特征維數(shù)。(3)針對復雜環(huán)境下的交通標志圖像提出一種新的普適的檢測方案。對交通標志圖像分為高亮度、中亮度與低亮度三類,每一類設計合適的檢測方法;在交通標志精確檢測步驟中對候選區(qū)域分為高亮度、中亮度、低亮度三類,針對不同類別分別訓練出相應的SVM分類器:High_SVM、Medium_SVM、Low_SVM。對復雜的場景進行分類處理可以在一定程度提高檢測方法的適應性。
[Abstract]:In recent years, more and more researchers have begun to pay attention to the intelligent transportation system (ITS), and traffic sign detection is an important part of the intelligent transportation system, and it is the premise of traffic sign recognition, which has important research significance and application value. In the face of relatively complex natural scene, traffic sign detection is not yet mature practical application, so it is necessary to continue to study a general detection scheme for traffic signs in complex scenarios. In this paper, the traffic sign detection algorithm in complex environment is studied. The purpose of this paper is to propose a detection scheme that is applicable to all kinds of scenarios in practical application. The main work of this paper is as follows: (1) several traffic sign segmentation methods based on color space are compared and analyzed. An adaptive threshold segmentation method based on RGB color space (RGBAT (RGB Color Space Adaptive Threshold).) is proposed to overcome the defects of the fixed threshold color segmentation method. This method can effectively overcome the defects of the fixed threshold segmentation method which are easily affected by illumination, flag fading, shadow and other factors. The effectiveness of RGBAT method is verified by comparing it with several traffic sign segmentation methods based on color space. (2) the gradient histogram feature HOG and HSV quantization histogram feature are simplified according to the traffic sign detection application. The feature dimension is reduced while the classification ability of the feature is guaranteed. (3) A new universal detection scheme for traffic sign images in complex environments is proposed. The traffic sign image can be divided into three categories: high brightness, medium brightness and low brightness. In the process of accurate detection of traffic signs, candidate regions are divided into three categories: high brightness, medium brightness and low brightness. The corresponding SVM classifier, High_SVM,Medium_SVM,Low_SVM., is trained for different categories. Classification of complex scenes can improve the adaptability of detection methods to some extent.
【學位授予單位】：河北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41

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本文編號：2405739