摘要
通过对钢板表面信息灰度值的详细分析,提出了一种利用简单感知器组成的分类器识别钢板表面缺陷的新方法。该方法在图像采集设备中直接对图像信息进行预处理,把认为是疑似缺陷信息的数据发送给后续处理器分析,可极大地减少系统数据处理量。在FPGA中实现了该方法,结果表明利用该方法简化了系统硬件结构,提高了系统实时性。
A novel method was presented to separate defective information from steel sheet surface images by anatomizing the grey values of steel sheet surface images, which uses a classifier composed of some simple perceptrons. The method directly preprocesses image data in camera, and only provides defective data out to be further processed, so the amount of data in the system which must be further processed is reduced dramatically. Furthermore, the method was implemented in FPGA, and experiment results show that after using this method, the system hardware structure is simple, as well as the real-time performance is improved greatly.
出处
《光电工程》
EI
CAS
CSCD
北大核心
2006年第11期45-48,119,共5页
Opto-Electronic Engineering
关键词
感知器
钢板
表面缺陷
信息分类
FPGA
Perceptron
Steel sheet
Surface defect
Information classification
FPGA
