Detection of Cigarette Missing in Packing Based on Deep Convolutional Neural Network
Market research shows that one of the most intolerable issues in the pack of cigarettes is the cigarette missing.This issue makes substantial adverse effects on a company which needs to be avoided completely.Existing research uses a weight detection method to identify packages with issues.However,the accuracy of weight detection methods is low due to instrument error and complex workshop environment.Nowadays,deep learning is a hot topic in the area of detection and classification.The input data for this work is the X-ray image which provides an opportunity to detect cigarette missing based on image classification.This work aims to propose a new detection method based on convolutional neural network.The convolutional neural network,which is the most popular deep learning method in the field of images,performs well when hand-crafted features are difficult to obtain.Besides the cigarette missing detection,this paper also succeeds to detect the packet damage,packet missing and cigarette misplaced.Experiments show that the convolutional neural network outperforms all other classical classifiers and the accuracy of cigarette missing detection is more than 99%.The proposed method is a promising cigarette missing detection method and can be used for the detection of packet damage,packet missing and cigarette misplaced.
Cigarette missing X-ray image Convolutional neural network Deep learning
Wei Ying Xiaojun Hu Liang Zhao
Hangzhou Cigarette Factory,China Tobacco Zhejiang Industrial,CO.Ltd,Hangzhou,China
国际会议
重庆
英文
1252-1256
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）