智能挖掘机视觉系统中图像处理技术研究
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摘要
随着陆地资源紧张程度的加剧,人类正向着海洋、太空等更广阔的领域迈进。无论是在水下进行挖掘作业还是在太空进行资源开采,其环境都是十分复杂、恶劣的,甚至有辐射或放射性污染,操作者若直接介入,人身安全得不到保证,而通过智能挖掘机代替人完成这些危险工作是一个理想的方案。因此,智能挖掘机的研究工作具有很重要的理论价值和工程意义。而视觉系统作为实现智能挖掘机所必须的一部分,其重要性不言而喻。
本文从挖掘机的应用角度出发,针对视觉系统中图像处理的几个关键技术性问题做了系统、深入的理论分析,提出了相应的改进算法,并针对挖掘机工作中的典型步骤,分析了改进算法的应用情况。本文的主要工作有以下几方面：
(1)分析了传统Chan Vese分割算法不能同时平滑噪声和保护弱边缘信息的原因,提出基于各向异性平滑的图像分割算法。分割模型中引入各向异性平滑项,并利用梯度信息对初始轮廓线进行设置。实验结果显示,改进的分割算法可以在平滑噪声的同时保护弱边缘信息,提高对弱边缘的捕捉能力,且利用梯度信息设置初始轮廓线可以大人减少不必要的运算量。
(2)针对现有全局轮廓匹配方法无法匹配受遮挡目标的不足,提出基于分段轮廓曲率的局部轮廓匹配算法。以曲率极值点为分段点进行轮廓分段处理,用每段轮廓上各点的曲率构造特征向量,采用微分进化算法处理寻优问题。实验结果显示,利用分段轮廓曲率特征进行匹配可以正确匹配受遮挡的目标,采用微分进化算法处理寻优问题,可以提高寻优速度,并保证获得全局最优解。
(3)分析了现有遮挡区域检测方法精度低的原因,提出基于联合特征的遮挡区域检测算法。利用图像中的颜色信息和运动信息进行遮挡区域检测,并采用双边滤波方法,处理遮挡区域的运动不连续问题。实验结果显示,利用颜色信息和运动信息进行联合判定,可提高遮挡区域检测的精度。
(4)针对标准Mean Shift跟踪算法特征表述没有引入像素点空域信息的不足,提出基于空域一颜色域特征表述的跟踪算法。把模板和目标区域分为重叠的子块,分别估计每个子块内像素点的空域一颜色域分布,从而引入空域信息。实验结果显示,引入像素点空域信息后,可以克服传统加权颜色直方图模型表述能力差的缺陷,提高跟踪精度。
(5)以挖掘机回转卸载过程为例,详细分析了本文改进算法在实际工作中的应用情况,用实验手段验证了本文改进的图像处理算法的可行性,最后讨论了智能挖掘机系统的信息融合问题。
With the lack of terrestrial resources as well as the continuous advancement of technology, human beings exploit into a wider area such as the sea and space. Due to restrictions of the environmental conditions, underwater operations and space development are very difficult to achieve manually. Furthermore, with the complexity of handling radioactive substances in the poor and dangerous environment, the operator intervention is inconvenience or the safety for the operator can not be guaranteed, so the intelligent excavator is a substitute to complete these complex and dangerous works. Therefore, the research on intelligent excavator has important theoretical value and engineering significance. The visual system as a necessary to achieve intelligent excavator, its importance is self-evident.
This paper makes a systematic and in-depth theoretical analysis for several image processing technical of the visual system to the application of an intelligent excavator, some improved algorithms have been proposed, at the same time, the application of the visual system has been expounded aimed at the concrete excavator's work. The main works in this paper are as follows:
(1) Analyzing the traditional Chan Vese image segmentation model which can not smooth the noise and preserve the weak edge at the same time, an improved segmentation algorithm is proposed. It introduces the anisotropic smoothing term to the segmentation model, and uses of edge detection operator to mount the initial contour. The experiments results show that, the noise is filtered and the weak edges are preserved at the same time. The capability to capture the outline on the target is enhanced. Meanwhile the initial contour mount method can speed up the operation speed.
(2) Aiming at the current global contour matching method which can not match under the occluded case, a local contour matching algorithm is proposed which is based on section contour curvature. First, compute the curvature of each point on the contour, and choose the candidate points which meet the threshold condition. After that segment the contour based on the points. The feature vectors are constructed by using these sub-contours. The variance is used for measuring the similarity between the model contour and the target contour. The differential evolution algorithm is adopted to deal with the issue of optimization. The experiments results show that, the improved method can cope with that the global contour metching method can not obtain the metching result when the contour is occluded, and using the differential evolution algorithm can speed up the operation, meanwhile the optimal result can be obtained.
(3) After analyzing the problem of the large errors in optical flow estimation which is caused by low detection precision on occluded regions, the improved occlusion detection algorithm is put forward, and use for optical flow estimation. Use the joint features in the image, to detect the occluded regions, and to cope with the motion discontinuity by bilateral filtering. The results show that, the improved optical flow estimation algorithm can enhance the accuracy and robustness of the estimation on occluded regions, and can acquire the more precision optical flow information.
(4) Aiming at the the problem of easily locate the error position by the classic Mean Shift which is caused by no the pixels' space information in the feature space representation when there are similar colors in the background around the tracking object, an improved object tracking algorithm is proposed. At first, the target model region is segmented into overlapped square, and their spatial histograms are computed which has introduced pixels'spatial information into. So the weakness of the weighted color histograms is overcomed and the accuracy is enhanced. The experiment results show that the improved algorithm can track the object more robust, accurately and quickly.
(5) At the last, aiming at the turn and unload procedure of the excavator, the application of the improved slgorithms is discussed in the actual work. Meanwhile, the feasibility is testeland verifiaiby the experiment. And the issue for fusing the visual signals in intelligent control system on the intelligent excavator is discussed.
本文从挖掘机的应用角度出发,针对视觉系统中图像处理的几个关键技术性问题做了系统、深入的理论分析,提出了相应的改进算法,并针对挖掘机工作中的典型步骤,分析了改进算法的应用情况。本文的主要工作有以下几方面：
(1)分析了传统Chan Vese分割算法不能同时平滑噪声和保护弱边缘信息的原因,提出基于各向异性平滑的图像分割算法。分割模型中引入各向异性平滑项,并利用梯度信息对初始轮廓线进行设置。实验结果显示,改进的分割算法可以在平滑噪声的同时保护弱边缘信息,提高对弱边缘的捕捉能力,且利用梯度信息设置初始轮廓线可以大人减少不必要的运算量。
(2)针对现有全局轮廓匹配方法无法匹配受遮挡目标的不足,提出基于分段轮廓曲率的局部轮廓匹配算法。以曲率极值点为分段点进行轮廓分段处理,用每段轮廓上各点的曲率构造特征向量,采用微分进化算法处理寻优问题。实验结果显示,利用分段轮廓曲率特征进行匹配可以正确匹配受遮挡的目标,采用微分进化算法处理寻优问题,可以提高寻优速度,并保证获得全局最优解。
(3)分析了现有遮挡区域检测方法精度低的原因,提出基于联合特征的遮挡区域检测算法。利用图像中的颜色信息和运动信息进行遮挡区域检测,并采用双边滤波方法,处理遮挡区域的运动不连续问题。实验结果显示,利用颜色信息和运动信息进行联合判定,可提高遮挡区域检测的精度。
(4)针对标准Mean Shift跟踪算法特征表述没有引入像素点空域信息的不足,提出基于空域一颜色域特征表述的跟踪算法。把模板和目标区域分为重叠的子块,分别估计每个子块内像素点的空域一颜色域分布,从而引入空域信息。实验结果显示,引入像素点空域信息后,可以克服传统加权颜色直方图模型表述能力差的缺陷,提高跟踪精度。
(5)以挖掘机回转卸载过程为例,详细分析了本文改进算法在实际工作中的应用情况,用实验手段验证了本文改进的图像处理算法的可行性,最后讨论了智能挖掘机系统的信息融合问题。
With the lack of terrestrial resources as well as the continuous advancement of technology, human beings exploit into a wider area such as the sea and space. Due to restrictions of the environmental conditions, underwater operations and space development are very difficult to achieve manually. Furthermore, with the complexity of handling radioactive substances in the poor and dangerous environment, the operator intervention is inconvenience or the safety for the operator can not be guaranteed, so the intelligent excavator is a substitute to complete these complex and dangerous works. Therefore, the research on intelligent excavator has important theoretical value and engineering significance. The visual system as a necessary to achieve intelligent excavator, its importance is self-evident.
This paper makes a systematic and in-depth theoretical analysis for several image processing technical of the visual system to the application of an intelligent excavator, some improved algorithms have been proposed, at the same time, the application of the visual system has been expounded aimed at the concrete excavator's work. The main works in this paper are as follows:
(1) Analyzing the traditional Chan Vese image segmentation model which can not smooth the noise and preserve the weak edge at the same time, an improved segmentation algorithm is proposed. It introduces the anisotropic smoothing term to the segmentation model, and uses of edge detection operator to mount the initial contour. The experiments results show that, the noise is filtered and the weak edges are preserved at the same time. The capability to capture the outline on the target is enhanced. Meanwhile the initial contour mount method can speed up the operation speed.
(2) Aiming at the current global contour matching method which can not match under the occluded case, a local contour matching algorithm is proposed which is based on section contour curvature. First, compute the curvature of each point on the contour, and choose the candidate points which meet the threshold condition. After that segment the contour based on the points. The feature vectors are constructed by using these sub-contours. The variance is used for measuring the similarity between the model contour and the target contour. The differential evolution algorithm is adopted to deal with the issue of optimization. The experiments results show that, the improved method can cope with that the global contour metching method can not obtain the metching result when the contour is occluded, and using the differential evolution algorithm can speed up the operation, meanwhile the optimal result can be obtained.
(3) After analyzing the problem of the large errors in optical flow estimation which is caused by low detection precision on occluded regions, the improved occlusion detection algorithm is put forward, and use for optical flow estimation. Use the joint features in the image, to detect the occluded regions, and to cope with the motion discontinuity by bilateral filtering. The results show that, the improved optical flow estimation algorithm can enhance the accuracy and robustness of the estimation on occluded regions, and can acquire the more precision optical flow information.
(4) Aiming at the the problem of easily locate the error position by the classic Mean Shift which is caused by no the pixels' space information in the feature space representation when there are similar colors in the background around the tracking object, an improved object tracking algorithm is proposed. At first, the target model region is segmented into overlapped square, and their spatial histograms are computed which has introduced pixels'spatial information into. So the weakness of the weighted color histograms is overcomed and the accuracy is enhanced. The experiment results show that the improved algorithm can track the object more robust, accurately and quickly.
(5) At the last, aiming at the turn and unload procedure of the excavator, the application of the improved slgorithms is discussed in the actual work. Meanwhile, the feasibility is testeland verifiaiby the experiment. And the issue for fusing the visual signals in intelligent control system on the intelligent excavator is discussed.
引文
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