中文摘要：

针对复杂生产流通过程中,传统算法无法对因防护不当和磨损污染等原因造成的金属刀具表面二维条码缺损和磨损等失效问题进行定位和识别的缺陷,设计了一个基于多信息融合的失效条码识别系统,完成刀具产品的识别和条码信息的提取.该系统利用图像传感器和重量传感器对刀具形状、残余条码纹理和重量等特征进行量化,提取高维特征向量.通过支持向量机与证据理论相结合,实现对失效条码的分类识别.实验结果表明,该系统能够对条码存在污损的刀具进行准确、快速地分类和识别,满足实际生产中的要求.

英文摘要：

A multi information fusion classification and identification system was proposed in view of the fact that traditional tool identification methods suffered from inefficiency and being susceptible to the bar code failure due to inadequate protection,pollution and other factors in the process of complex production and circulation.First,this system quantized tool features,such as shape,texture,weight and other characteristics,from image sensors and weight sensors.Then,high dimension features vector from both training and testing samples of tool and bar code was extracted.Finally the failure barcode was obtained with the algorithms of support vector machine and Dempster-Shafer.The experimental results show that the system could classify and identify the tool of destructive bar code accurately and effectively which can satisfy the actual requirement in production.