基于层次特征提取与几何模型辅助的货车故障轨边图像识别方法研究

项目名称： 基于层次特征提取与几何模型辅助的货车故障轨边图像识别方法研究

项目编号： No.51205115

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 机械工程学科

项目作者： 孙国栋

作者单位： 湖北工业大学

项目金额： 25万元

中文摘要： 传统人工列车技术检查效率低，劳动强度大，且作业质量难以保证。虽然货车故障轨边图像检测系统实现了故障图像的采集与处理，但目前以人眼为主的故障判别依旧效率较低。本项目拟揭示车辆CAD模型与图像像素的匹配原则，提出几何特征辅助的零件图像识别与特征修正方法，以定位因光照、遮挡等干扰导致仅依靠灰度特征难以分割的零件；针对车辆零部件空间分布的层次性，探索基于Otsu多阈值分割的区域空间频数层次特征提取方法，并剖析其与形状特征、纹理特征间耦合关系，研究基于ReliefF的特征选择方法以剔除冗余特征。在此基础上，研究不等权重的相似度评估与基于k-邻近模型的案例推理，并融合规则推理设计基于混合推理机制的故障判别算法；遵循基于过车信息与故障类别的并行检测策略，最终实现高效、高识别率的货车故障轨边图像自动识别系统，对推进列检向完全机控方式转变，具有解决实际技术难题和探索全自动视觉检测理论与方法的双重意义。

中文关键词： 视觉检测；几何模型辅助零件识别；层次特征提取；混合推理机制；并行优化

英文摘要： Traditional artificial train inspection approach is inefficient, labor-intensive, and can not guarantee operating quality. The Trouble of moving Freight car Detection System has achieved the acquisition and processing of fault images, but the human eye-based fault discrimination is still less efficient. The project is planned to reveal the matching principle of the CAD model and the image pixels of train vehicles. And the parts image recognition and feature correction method based on geometric characteristics auxiliary is proposed, in order to locate the parts which it is difficult to segment only by the gray features due to the light and shelter interferences. According to the spatial distribution hierarchy of vehicle parts, the layer feature extraction method of regional spatial frequency will be explored based on Otsu multi-threshold segmentation. Then analyzing the coupled relationship among various layer features, shape features and texture features, the features selection method based on ReliefF algorithm is presented to eliminate redundant features. Based on study above, unequal weight similarity assessment and case-based reasoning adopting k-adjacent model are studied, and the hybrid reasoning mechanism for fault discrimination is designed by integrating rule-based reasoning. Following the parallel detec

英文关键词： vision inspection；geometric model aided parts recognition；layer feature extraction；hybrid reasoning mechanism；parallel optimization