| 杜年丰,史泽地,陈 伟,杨增汪.基于机器视觉的障碍物危险层次划分系统[J].电子测量与仪器学报,2021,35(6):17-26 |
| 基于机器视觉的障碍物危险层次划分系统 |
| Obstacle dangerous level classification system based on machine vision |
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| DOI： |
| 中文关键词: 特征提取 主成分分析 滤波器 危险层次分类 |
| 英文关键词:feature extraction principal component analysis filter hazard classification |
| 基金项目:国家自然科学基金资助项目(2207050076)、江苏省重点研发计划项目(BE2019033)、江苏省研究生科研与实践创新计划项目(SJCX20_0921)、徐州市重点研发计划项目(KC18144)、江苏师范大学实验室建设与管理项目(L2020Z03) |
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| 摘要点击次数: 712 |
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| 中文摘要: |
| 针对传统的障碍物危险层次划分时出现的过量周期、纬度灾难的问题,该系统采用对称性的 FIR 滤波器,这种方式利用
对称性优化滤波器系数只需要 N/ 2 个乘法和 N 个加法,可以生成自适应的障碍物数据,缩短滤波器响应的时间;在维度灾难的
问题中,该系统采用主成分分析的方式实现降维,在考虑障碍物的危险程度、距离、运动状态等多种特征参数的情况时,将两个
特征归一为一个数据集进行处理,这样就可以在改善分类超平面灵活性的同时,缩短了对障碍物危险层次划分所需要的时间。
结果证明该系统的算法运算时间缩短至 0. 25 s,危险程度中高类障碍物识别准确率达 96. 80%。 |
| 英文摘要: |
| Aiming at the problems of excessive cycles and latitude disasters in the traditional obstacle hazard division, the system uses a
symmetric FIR filter. In this way, using symmetry to optimize the filter coefficients only requires N/ 2 multiplications and N additions. It
can generate adaptive obstacle data and shorten the response time of the filter; in the problem of dimensional disaster, the system uses
principal component analysis to achieve dimensionality reduction, considering the danger degree, distance, movement state of the
obstacle, etc. In the case of a feature parameter, the two features are unified into one data set for processing, so that while improving the
flexibility of the classification hyperplane, it also shortens the time required for the classification of obstacles. The result proves that the
calculation time of the algorithm of the system is shortened to 0. 25 s, and the accuracy of the recognition of high-risk obstacles reaches
96. 80%. |
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