摘要
针对铝塑泡罩胶囊的颜色、大小和图片噪声等造成检测效果较差问题,提出基于GoogLeNet网络模型的铝塑泡罩胶囊包装缺陷检测方法。首先以药板批号区域为模板,利用归一化积相关灰度匹配法定位待检测药板,然后通过改进的灰度值投影法分割药板的胶囊泡罩区域,制作铝塑泡罩胶囊数据集,对改进的GoogLeNet网络模型进行训练和测试,实现铝塑泡罩胶囊的缺粒、胶囊凹帽,胶囊双帽缺陷识别。实验结果表明,改进的水平-垂直投影算法,对胶囊泡罩区域的分割准确率达到100%,网络对缺陷识别的召回率均在98.64%以上。改进的灰度值投影法算法鲁棒性高,分割效果较好;改进的网络对铝塑泡罩胶囊药板包装缺陷识别准确率相比较其他方法有明显提高,可应用于铝塑泡罩药板包装质检。
A defect detection method for aluminum-plastic blister capsule packaging based on GoogLeNet network model is proposed in order to address the poor detection effect brought on by the color,size,and picture noise of aluminum-plastic blister capsules.To locate the Plate batch number region to be detected,the normalized product correlation gray scale matching method is first used.Next,the drug plate's capsule blister region is divided by the improved gray value projection method,and the dataset for aluminum-plastic blister capsules is created.Finally,the improved GoogLeNet network model is trained and tested to realize the defect recognition of the missing grain,concave cap,and other defects.The experimental findings demonstrate that the improved horizontal-vertical projection method achieves 100%segmentation accuracy for capsule blister area,and the recall rate of network for defect recognition is over 98.64%.The improved gray value projection technique offers excellent segmentation capabilities and strong robustness.The improved network,which can be used for the quality inspection of aluminum-plastic blister tablet packaging,has considerably increased the accuracy of fault identification of aluminum-plastic blister capsule pharmaceutical board packaging when compared to previous methods.
作者
杨桂华
戴志诚
Yang Guihua;Dai Zhicheng(Guilin University of Technology College of Mechanical and Control Engineering,Guilin 541006,China)
出处
《电子测量技术》
北大核心
2023年第20期140-147,共8页
Electronic Measurement Technology
基金
国家自然科学基金地区基金项目(52065016)
广西研究生教育创新计划项目(JGY2021091)资助
作者简介
杨桂华,副教授,硕士生导师,研究方向为计算机检测与控制。E-mail:954991219@qq.com;通信作者:戴志诚,硕士研究生,研究方向为计算机检测与控制。E-mail:Mr.D-mail@qq.com
