摘要
交通标志检测和分割是未来智能交通的一种重要组成部分,是辅助驾驶和自动驾驶技术的一个重要模块。交通标志检测核心技术在于算法。目前,尽管计算机技术和人工智能技术在飞速发展,目标检测和目标识别的算法在不断的涌现,但交通标志具有多类性、周围环境的多变性和复杂性等特点,导致现有算法应用于研究交通标志检测和分类时存在很大的困难。
实际交通标志周围环境的复杂性等因素常常导致不完整交通标志出现,这给检测带来巨大的困难,而现有的检测算法并不能很好的处理上述问题。实现交通标志特征的有效选择和提取是交通标志分类的一个关键性前提条件,到目前为止还没有一个学者从理论上证明或者实验上证实最优特征的存在。如何实现交通标志的分类是交通标志识别的另一个关键技术,追求分类算法的鲁棒性和有效性仍然是尚未有效解决的热点问题。
针对以上不足,本文研究并提出了在复杂背景下基于颜色概率模型和局部模板的交通标志检测算法以及基于隐形状模型的检测算法；研究并提出了基于Gabor小波变换的交通标志特征提取、基于改进的MPCA和改进的MDA的特征选择算法；研究并提出了基于上述特征的AdaBoost分类算法。具体而言,论文的主要研究工作如下：
(1)在分析现在交通标志检测算法的基础上,提出基于颜色概率模型的方法来处理自然场景下采集的交通标志颜色失真性问题,克服了直接采用各类颜色模型的局限性,提高了交通标志检测的准确率；在分析形状匹配算法基础上,提出了基于局部模板的检测方法,克服了全局模板无法检测不完整交通标志的局限性,达到能检测不完整交通标志的目的。针对上述两个方面,分别进行了对比实验,实验证明了基于颜色概率模型和局部模板方法在检测交通标志方面的可行性和有效性。
(2)在研究路面交通标志快速检测算法的基础上,针对现在交通标志检测算法在时效性和检测率方面的不足,提出基于隐形状模型的交通标志检测方法。隐形状模型从像素点及其局部出发,克服了常用模板匹配方法在时间开销上的缺陷,大大提高了检测的速度；自上而下的分割方式解决了交通标志常用检测方法在分割过程中容易出现像素误分割的问题,提高了分割的准确率。由于该方法在分割过程中是从局部出发,因此,它还能实现对不完整交通标志的有效分割。最后通过实验验证了此方法在分割过程中的高效性。
(3)在交通标志实现有效分割的基础上,研究交通标志的频域分布特征,根据Gabor小波变换良好的空间和频率局部化特征以及在多分辨率方面的特性,研究了Gabor小波变换在特征提取方面的效果,考察了Gabor小波变换在提取交通标志频域和空域信息时的特性,重点分析了Gabor小波变换在提取交通标志影像特征过程中分别对边缘、亮度和特征矩等方面的响应,提出了基于Gabor小波变换的特征提取,并计算获取了交通标志的Gabor特征,最后通过实验验证了Gabor特征作为交通标志识别的特征向量的可行性。
(4)在研究复杂数据消除冗余度和降维过程中,分析了PCA在多线性代数领域的推广形式MPCA在消除冗余度和降维方面的效果,重点考察了MPCA算法的理论基础和算法的迭代实现过程,提出了一种改进的MPCA算法,在理论上证明了改进的MPCA算法在消除冗余度方面更优于原MPCA,在处理速度方面效果更优于原MPCA算法,同时还考察了改进的MPCA算法和PCA算法以及2DPCA算法在处理数据方面的能力。最后分别通过对比实验,验证了改进的MPCA算法在处理速度和消除数据之间的冗余度方面优越性。
(5)在研究复杂数据消除冗余度和降维的过程中,考虑到数据在特征空间上各类别之间存在的差异性,分析了LDA在多线性代数领域的推广形式MDA在消除冗余度和降维方面的效果,重点考察了MDA算法的理论基础和算法的迭代实现过程,完善了MDA算法的理论基础,充实了MDA算法,提出了一种新的MDA算法,改善了MDA算法的速度；在分析MDA算法的过程中,提出了一种改进的MDA算法,并在理论上证明了改进的MDA算法在消除冗余度方面更优于原MDA,在处理速度方面效果更优于原MDA算法,同时还考察了改进的MDA算法和LDA算法以及2DLDA算法在处理数据方面的能力。最后分别通过对比实验,验证了两类改进的MDA算法在处理速度和消除数据之间的冗余度方面优越性。
(6)通过分析改进的MPCA算法和改进的MDA算法在消除冗余度方面的效果和区别,研究了改进的MPCA和MDA的融合方法在消除特征相关性方面的特性,提出了一种融合改进的MPCA和MDA的特征选择方法,实现了特征空间数据冗余度的有效消除。最后通过实验验证了该方法在消除交通标志特征数据冗余度和特征提取方面的效果。
(7)在研究现有分类算法的基础上,从理论和算法的实现角度重点考察了AdaBoost算法的强大分类能力,以及AdaBoost在交通标志分类方面的应用,提出了以BP神经网络作为弱分类器,分别以Gobor特征、改进的MPCA特征、改进的MDA特征、融合改进的MPCA和MDA特征、融合改进的MPCA和MDA提取的Gabor特征作为输入特征的AdaBoost自动分类算法。最后通过对比实验,验证了上述提出的算法的可行性。
Traffic signs detection and segmentation is an important research domain in ITS (Intelligent Transportation System) and part of the key components in Driving Assistance System and automatic driving technology in the future. At present, in the pace of rapid develop of computer technology and artificial intelligence technology, target detection and target recognition algorithm have been brought out and improved. However, because of the many types of traffic signs, the variability and complexity of the surrounding environment and the specific nature of the problem, it is necessary to research the algorithm of traffic signs detection and segmentation.
Traffic sign data are required to be collected from both sides of the actual road. The complexity of environment and any various factors may lead to emergement of incomplete traffic sign and the existing detection algorithm can't handle the issue well. How to achieve effective feature of traffic signs is the key procedure in order to realize the well classification results of traffic sign. So far, the existence of the best have not been proved in the theory or verified by experiment. And the pursuit to robustness and effectiveness of algorithm still is a hot issue in the traffic signs classification. Research in this area has always remained constant development.
This paper makes the following researches according to the above descriptions and problems.
(1) Based on the research and analysis of existing algorithm of traffic signs detection, a detection algorithm is proposed base on the color probabilistic model approach. It can been used to rectify the color distortion of traffic sign collected from the natural scene, overcome the limitations of various models and improve the detection accuracy; in the analysis of shape matching templates, a local templates matching method is proposed to overcome the drawback of global templates matching method which incomplete traffic sign is not detected. In response to these aspects proposed, respectively, experiments have proved that the traffic signs detection algorithm based on the color probabilistic model and local templates is feasible and effective.
(2) In the study of existing algorithm of traffic signs, the Implicit Shape Model(ISM) is proposed to solve the timeliness and lack of detection rate of existing algorithms. The main idea of ISM is to integrate recognition and segmentation into a common probabilistic framework and generate a per-pixel confidence measure specifying the area that supports a hypothesis and how much it can be trusted. It not only overcomes the shortcomings of the inefficiency of previously published detection methods of traffic signs but also has the ability to detect the incomplete traffic signs under significant partial occlusion. Finally, we present the test results which show the proposed method can efficiently detect and segment the traffic signs from the natural scenes.
(3) The frequency distribution feature of traffic sign is studied after achieving the effective segment. According to the good localization characteristics of Gabor wavelet transform between spatial and frequency, as well as multi-resolution characteristics, effect which Gabor wavelet extracts object feature is focus to study, especially traffic sign object. We mainly emphasis on the edge of the process, the response of intensity and character moments of traffic signs, proposed a feature extraction based on the Gabor wavelet transform, and calculate and obtain the Gabor features of traffic signs. Finally, experiment verifies the Gabor features as the traffic sign recognition feature vector is feasible.
(4) By studying the process to eliminate redundancy and reduce dimensionality of complex data, MPCA (Multilinear Principal Component Analysis), the extended form of PCA (Principal Component Analysis) in the multilinear algebra field, is studied, and theoretical basis of MPCA algorithm and iterative implementation process of algorithm are emphasized on. A new improved proposed MPCA algorithm is theoretically proved to superior to the original MPCA algorithm in data redundancy elimination, as well as processing speed. At the same time, we also compare to improved MPCA algorithm's ability to process data to PCA and 2DPCA. Finally, by comparing the experiments were to verify the improved MPCA algorithm's superiority in processing speed and eliminating data redundancy.
(5) By studying the process to eliminate redundancy and reduce dimensionality of complex data based on the distinction between the various categories and between the various samples, MDA (Multilinear Discriminant Analysis), the extended form of LDA (Linear Discriminant Analysis) in the multilinear algebra field, is studied, and theoretical basis of MDA algorithm and iterative implementation process of algorithm are emphasized on. On this basis, the theoretical basis of MDA is enriched and a new iterative process is proposed to improve the speed of the processing. Then, in the in-depth analysis of MDA, a new improved proposed MDA algorithm is theoretically proved to superior to the original MDA algorithm in data redundancy elimination, as well as processing speed. At the same time, we also compare to improved MDA algorithm's ability to process data to LDA and 2DLDA. Finally, by comparing the experiments were to verify two types of improved MDA algorithm's superiority in processing speed and eliminating data redundancy.
(6) By analyzing the effectiveness and differences of eliminating redundancy and reducing dimensionality between improved MPCA and improved MDA, a new feature selection method, fusion of improved MPCA and improved MDA, is advanced to realize the effective compression of feature space dimension and elimination of redundancy. Finally, experiments show that the proposed method confirms the correctness of the theoretical analysis.
(7) Based on the study of existing classification algorithms, we notice that an AdaBoost algorithm, one of top 10 classification algorithms, has a very powerful classification capability and little application in the classification of traffic signs. AdaBoost classification in the application of traffic sign is presented, whose weak classifier is BP neural network and whose input feature vectors are Gabor feature, the improved MPCA feature, the improved MDA, the Integration of improved MPCA and improved MDA and Gabor feature of integration of improved MPCA and MDA, respectively. Finally, contrast experiments verify the feasibility of the proposed algorithm.
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