张志晟,张雷洪,王新月,李正礼,孙琳源,徐邦联.基于GRNN-PNN神经网络的印铁缺陷分类方法[J].包装工程,2020,41(15):260-266.
ZHANG Zhi-sheng,ZHANG Lei-hong,WANG Xin-yue,LI Zheng-li,SUN Lin-yuan,XU Bang-lian.Classification Method of Printed Iron Defects Based on GRNN-PNN Neural Network[J].Packaging Engineering,2020,41(15):260-266. |
| 基于GRNN-PNN神经网络的印铁缺陷分类方法 |
| Classification Method of Printed Iron Defects Based on GRNN-PNN Neural Network |
| 投稿时间：2019-12-02 修订日期：2020-08-10 |
| DOI：10.19554/j.cnki.1001-3563.2020.15.039 |
| 中文关键词: 缺陷检测 图像评估 神经网络 印铁技术 图像处理 |
| 英文关键词: defect detection image evaluation neural network iron printing technology image processing |
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| 中文摘要: |
| 目的 针对在印铁过程中缺陷检测系统存在不同缺陷类型检测精度不高，对于产品整体质量无法实现智能判断的问题，基于GRNN-PNN神经网络，提出一种适用于印铁在线检测的分类算法。方法 对平面印刷铁片进行小波变换提取低频信息，在低频信息中进行缺陷定位并对缺陷区域进行标记和分割。通过缺陷面积、周长等评价指数和缺陷形状构建GRNN神经网络，对缺陷进行分类。通过构建PNN神经网络智能化判别整体产品是否属于合格产品。结果 GRNN-PNN平均耗时0.69 s，达到了厂方对于缺陷在线检测的响应时间要求。GRNN-PNN多分类的准确率为86%，能够对印铁过程中产生的主要缺陷进行分类。二分类的灵敏度为96%，可以准确地判断产品整体的合格性。在5%的椒盐噪声干扰下，准确率为63%，具有良好的鲁棒性。结论 该设计能够对印铁缺陷进行精确的分类和智能的判断，GRNN-PNN神经网络可以在印铁过程中进一步提高检测精度，GRNN-PNN神经网络可帮助质检员及时判断生产质量。 |
| 英文摘要: |
| During the iron printing process, the defect detection system has a problem that the accuracy of defect detection is not high, and intelligent judgment cannot be achieved for the overall product quality. This paper aims to propose a classification algorithm suitable for on-line detection of printed iron based on GRNN-PNN neural network. Wavelet transform was performed on printed iron sheet to extract low-frequency information. The defects in low-frequency information were located, and the defective areas were marked and segmented. A GRNN neural network was constructed based on defect area, perimeter and other evaluation indexes to classify defects. A PNN neural network was constructed to intelligently determine whether the overall product was a qualified product. The average time of GRNN-PNN was 0.69s, which met the factory's requirement on the response time of online defect detection. The accuracy of GRNN-PNN multi-classification was 86%, which can classify the main defects generated during the iron printing process. The sensitivity of the two classifications was 96%, which can accurately judge the overall qualification of the product. Under 5% salt and pepper noise, the accuracy rate was 63%, and GRNN-PNN had good robustness. The design can accurately classify and intelligently judge the defects of printed iron. The GRNN-PNN neural network can further improve the detection accuracy during the iron printing process. The GRNN-PNN neural network can help quality inspectors to judge the production quality in time. |
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