交通视频中噪声图像分割技术研究
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摘要
本文围绕交通视频噪声图像分割在多功能路面检测车、智能车辆视觉导航中的应用,就图像去除噪声、路面裂缝检测、车道检测、边缘检测和运动目标检测等噪声图像分割问题开展研究,本文的贡献如下：
1.为了消除噪声对图像分割的影响,提高图像的信噪比,提出了自适应神经模糊推理系统与中值滤波相结合的去除噪声算法。将噪声图像、中值滤波对噪声点的估计和维纳滤波对噪声点的估计作为自适应神经模糊系统的三个输入,系统的输出为对像素点的判断,是噪声点还是真实的图像点,如果是噪声点,就进行中值滤波,如果是真实的图像,保持不变,直接输出,最终得到两者的融合去噪图像。仿真实验表明,本章算法滤波的结果比中值滤波的结果具有更高的信噪比。
2.针对路面视频光照不均、存在油污、破损和噪声等情况对路面裂缝分割带来的不利影响,提出了一种小波域内基于块的路面裂缝检测算法。路面图像经过去噪、增强等预处理后,通过haar小波变换,在最高层中对近似分量进行自适应阈值分割得到路面裂缝的初始区域。然后在其他各层中,从高层到低层依次只针对路面破损区域进一步分块分割处理,得到路面裂缝的准确检测。仿真实验表明,本文提出的算法可以很好的克服光照不均、存在油污、破损和噪声的影响,对不同的路面裂缝类型,都具有很好地路面裂缝分割效果。
3.车道分割受到光照、路面阴影、破损、油污和噪声的干扰,容易出现错误分割,为了准确地提取出符合人眼特性的车道边界,本文提出了基于视觉模型的车道检测。利用人眼的视觉特性,按照韦伯定律,提出了分段选取阈值,在HSV颜色空间内融合区域车道分割结果和边缘车道分割结果,得到车道的边缘点,再使用Hough变换给出了车道的位置和方向信息,为视觉导航提供控制参数。仿真实验结果表明,本算法具有抗噪性强、光照对检测结果影响较小,对路面存在的阴影、油污和破损等情况处理较好、具有运行较稳定等特点。
4.为了适应交通视频海量数据处理的需要,对图像分割自适应地选取阈值,提出了基于自适应神经模糊推理系统的边缘检测算法,该算法引入一个具有4个输入和1个输出的自适应神经模糊推理系统,选取了与边缘方向和梯度大小双重信息相关的4个目标函数作为系统的输入,目标函数值的大小与像素点是否为边缘点可能性的大小相关。采用计算机合成训练输入图像和训练目标图像,对自适应神经模糊推理系统进行训练,得到训练好的自适应神经模糊推理系统,再将定义好的4个目标函数作为自适应神经模糊推理系统的输入,得到系统的输出。对于自适应神经模糊推理系统的输出值,运用一个后处理程序,采用一个固定的阈值来判断该中心点是否为边缘点。仿真实验表明,该算法能够快速自适应地选取阈值,适合海量交通视频处理的需要,比较传统方法和当前文献中关于神经和模糊系统边缘检测的方法,边缘检测效果要好。
5.为了从带有噪声的交通视频中准确完整地分割出运动目标,假定“背景总是以较大的频率出现”,提出了一种基于双阈值顺序聚类的背景重构算法,算法分为三步：第一步,基于双阈值顺序聚类的思想分类像素的灰度类；第二步,执行合并过程；第三步,选择背景图像。该算法不需要预先知道场景的先验知识,能够直接从含有前景目标的图像中构建出多模态的场景；通过使用两个阈值Θ1,和Θ2(Θ2>Θ1)对输入数据进行聚类,增强了聚类结果对阈值取值的鲁棒性,降低了算法对阈值选取的敏感度；由于一个数据的分配要延迟到有足够的信息,所以对数据参与算法的顺序不是很敏感；在运行双阈值顺序聚类之后执行合并过程,避免了出现多个距离很近的聚类。对多种视频进行仿真实验,且与其他背景重构方法进行比较,背景重构更理想,运动目标检测更完整,更好地克服了噪声的影响。
The research of this paper is focused on the application of noise image segementation to the multi-function road surface status inspection vehicle and the intelligent vehicle. Based on the engineering background, the methods of transportation imges denoising, the road crack detection, lane detection and motion detection is studied. The main contents and contributions of this dissertation are as follows.
1. Image denoise technology.
A new impulse noise detector based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filtering, a wiener filtering and the ANFIS. The noise is exactly estimated through the proposed operator. The internal parameters of the ANFIS are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The distinctive feature of the proposed operator is that it offers well line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.
2. Road crack detection technology.
We proposed a pavement distress detection algorithm based on tiles in wavelet domain. After the preprocesses of de-noise, enhancing, etc., pavement image was decomposed by Haar wavelet and the approximation coefficient in the highest layer was segmented by adaptive threshold to get the initial pavement distress regions. Then in the other ordinal layers, from the higher layer to the lower layer, only the pavement distress regions were segmented tiles to gain the exact detection of pavement distress. Simulation show the algorithm proposed in this paper could detect pavement distress effectively for different types and could overcome the effect of the noisy.
3. lane detection technology.
In the lane detection, we propose a method based on visual moden. The thresholds of segementation are calculated according to Weber's law and bionics principle. The lane detection fuses the way of lane edges detection and the method of color-based segmentation. The method reduces the shadow and noise points influnce on lane detection and is convencient in the applications in the vision navigation of the autonomous highway vehicle. The simulation results reveals that our method gets a better result in different road conditions.
4. edge detection technology.
Neuro-fuzzy (NF) systems are very suitable tools to deal with uncertainty encountered in the process of extracting useful information from images. We present a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. The internal parameters of the proposed ANFIS edge detector are optimized by training using very simple artificial images. The edges are directly determined by ANFIS network. The proposed ANFIS edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed ANFIS edge detector exhibits much better performance than the competing operators and may efficiently be used for the detection of edges in digital images.
5. background reconstruction technology.
A new background subtraction algorithm based on two thresholds sequential clustering is proposed in this paper. First, pixel intensity in period of time is classified based on two thresholds sequential clustering. Second, combination process is run to classified classes. Finally, the backgrounds of scene are selected, so the background model can represent the scene well. The simulation results show that the proposed algorithm is robust to the thresholds, those near classes are avoided at all, and the effect of input order of data has been reduced greatly. And the background model can represent the scene well.
1.为了消除噪声对图像分割的影响,提高图像的信噪比,提出了自适应神经模糊推理系统与中值滤波相结合的去除噪声算法。将噪声图像、中值滤波对噪声点的估计和维纳滤波对噪声点的估计作为自适应神经模糊系统的三个输入,系统的输出为对像素点的判断,是噪声点还是真实的图像点,如果是噪声点,就进行中值滤波,如果是真实的图像,保持不变,直接输出,最终得到两者的融合去噪图像。仿真实验表明,本章算法滤波的结果比中值滤波的结果具有更高的信噪比。
2.针对路面视频光照不均、存在油污、破损和噪声等情况对路面裂缝分割带来的不利影响,提出了一种小波域内基于块的路面裂缝检测算法。路面图像经过去噪、增强等预处理后,通过haar小波变换,在最高层中对近似分量进行自适应阈值分割得到路面裂缝的初始区域。然后在其他各层中,从高层到低层依次只针对路面破损区域进一步分块分割处理,得到路面裂缝的准确检测。仿真实验表明,本文提出的算法可以很好的克服光照不均、存在油污、破损和噪声的影响,对不同的路面裂缝类型,都具有很好地路面裂缝分割效果。
3.车道分割受到光照、路面阴影、破损、油污和噪声的干扰,容易出现错误分割,为了准确地提取出符合人眼特性的车道边界,本文提出了基于视觉模型的车道检测。利用人眼的视觉特性,按照韦伯定律,提出了分段选取阈值,在HSV颜色空间内融合区域车道分割结果和边缘车道分割结果,得到车道的边缘点,再使用Hough变换给出了车道的位置和方向信息,为视觉导航提供控制参数。仿真实验结果表明,本算法具有抗噪性强、光照对检测结果影响较小,对路面存在的阴影、油污和破损等情况处理较好、具有运行较稳定等特点。
4.为了适应交通视频海量数据处理的需要,对图像分割自适应地选取阈值,提出了基于自适应神经模糊推理系统的边缘检测算法,该算法引入一个具有4个输入和1个输出的自适应神经模糊推理系统,选取了与边缘方向和梯度大小双重信息相关的4个目标函数作为系统的输入,目标函数值的大小与像素点是否为边缘点可能性的大小相关。采用计算机合成训练输入图像和训练目标图像,对自适应神经模糊推理系统进行训练,得到训练好的自适应神经模糊推理系统,再将定义好的4个目标函数作为自适应神经模糊推理系统的输入,得到系统的输出。对于自适应神经模糊推理系统的输出值,运用一个后处理程序,采用一个固定的阈值来判断该中心点是否为边缘点。仿真实验表明,该算法能够快速自适应地选取阈值,适合海量交通视频处理的需要,比较传统方法和当前文献中关于神经和模糊系统边缘检测的方法,边缘检测效果要好。
5.为了从带有噪声的交通视频中准确完整地分割出运动目标,假定“背景总是以较大的频率出现”,提出了一种基于双阈值顺序聚类的背景重构算法,算法分为三步：第一步,基于双阈值顺序聚类的思想分类像素的灰度类；第二步,执行合并过程；第三步,选择背景图像。该算法不需要预先知道场景的先验知识,能够直接从含有前景目标的图像中构建出多模态的场景；通过使用两个阈值Θ1,和Θ2(Θ2>Θ1)对输入数据进行聚类,增强了聚类结果对阈值取值的鲁棒性,降低了算法对阈值选取的敏感度；由于一个数据的分配要延迟到有足够的信息,所以对数据参与算法的顺序不是很敏感；在运行双阈值顺序聚类之后执行合并过程,避免了出现多个距离很近的聚类。对多种视频进行仿真实验,且与其他背景重构方法进行比较,背景重构更理想,运动目标检测更完整,更好地克服了噪声的影响。
The research of this paper is focused on the application of noise image segementation to the multi-function road surface status inspection vehicle and the intelligent vehicle. Based on the engineering background, the methods of transportation imges denoising, the road crack detection, lane detection and motion detection is studied. The main contents and contributions of this dissertation are as follows.
1. Image denoise technology.
A new impulse noise detector based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filtering, a wiener filtering and the ANFIS. The noise is exactly estimated through the proposed operator. The internal parameters of the ANFIS are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The distinctive feature of the proposed operator is that it offers well line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.
2. Road crack detection technology.
We proposed a pavement distress detection algorithm based on tiles in wavelet domain. After the preprocesses of de-noise, enhancing, etc., pavement image was decomposed by Haar wavelet and the approximation coefficient in the highest layer was segmented by adaptive threshold to get the initial pavement distress regions. Then in the other ordinal layers, from the higher layer to the lower layer, only the pavement distress regions were segmented tiles to gain the exact detection of pavement distress. Simulation show the algorithm proposed in this paper could detect pavement distress effectively for different types and could overcome the effect of the noisy.
3. lane detection technology.
In the lane detection, we propose a method based on visual moden. The thresholds of segementation are calculated according to Weber's law and bionics principle. The lane detection fuses the way of lane edges detection and the method of color-based segmentation. The method reduces the shadow and noise points influnce on lane detection and is convencient in the applications in the vision navigation of the autonomous highway vehicle. The simulation results reveals that our method gets a better result in different road conditions.
4. edge detection technology.
Neuro-fuzzy (NF) systems are very suitable tools to deal with uncertainty encountered in the process of extracting useful information from images. We present a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. The internal parameters of the proposed ANFIS edge detector are optimized by training using very simple artificial images. The edges are directly determined by ANFIS network. The proposed ANFIS edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed ANFIS edge detector exhibits much better performance than the competing operators and may efficiently be used for the detection of edges in digital images.
5. background reconstruction technology.
A new background subtraction algorithm based on two thresholds sequential clustering is proposed in this paper. First, pixel intensity in period of time is classified based on two thresholds sequential clustering. Second, combination process is run to classified classes. Finally, the backgrounds of scene are selected, so the background model can represent the scene well. The simulation results show that the proposed algorithm is robust to the thresholds, those near classes are avoided at all, and the effect of input order of data has been reduced greatly. And the background model can represent the scene well.
引文
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