摘要
本文提出一种基于深度学习的识别方法用于医用塑瓶气泡、积料等生产缺陷的实时检测,设计工业现场的视觉检测硬件平台,细述积料与气泡检测算法的原理,简述算法检测前的图像预处理。在Pytorch框架下通过ResNet系列算法与MobilenetV2算法的正交实验对积料检测实时性能进行比较,同时优化RetinaNet网络在气泡上的检测性能。在生产现场中该方法关于积料的平均检测精度为99.7%,单幅图片检测时间为29.7 ms;气泡的Fβ指数为99.5%,单幅图片检测时间为35.5 ms,达到企业生产的要求。
This paper proposes a recognition method based on deep learning for the real-time detection of production defects such as medical plastic bottle bubbles and accumulated materials,designs the visual inspection hardware platform of the industrial site,describes the principle of the accumulation and bubble detection algorithm,and briefly describes the image pre-processing before the algorithm detection.Under the Pytorch framework,the real-time performance of aggregate detection is compared by orthogonal experiment between ResNet series algorithms and MobilenetV2 algorithm,and the detection performance of RetinaNet network on the bubbles is optimized.At the production site,the average detection accuracy of the proposed method is 99.7%and the single detection time is 29.7 ms.The Fβindex of the bubble is 99.5%and the single detection time is 35.5 ms,which meets the requirements of enterprise production.
作者
付磊
任德均
胡云起
郜明
邱吕
FU Lei;REN De-jun;HU Yun-qi;GAO Ming;QIU Lyu(School of Mechanical Engineering,Sichuan University,Chengdu 610065,China)
出处
《计算机与现代化》
2020年第4期104-108,共5页
Computer and Modernization
关键词
医用塑瓶
图像处理
深度学习
目标检测
medical plastic bottle
image processing
deep learning
target detection
作者简介
付磊(1995-),男,江西丰城人,硕士研究生,研究方向:机器视觉,E-mail:fuleijx2018@163.com;通信作者:任德均(1971-),男,四川成都人,副教授,硕士生导师,博士,研究方向:嵌入式系统,机电一体化,机器视觉,E-mail:rendejun@scu.edu.cn;胡云起(1995-),男,硕士研究生,研究方向:机器视觉,E-mail:1119063534@qq.com;郜明(1996-),男,硕士研究生,研究方向:机器视觉,E-mail:1204563110@qq.com;邱吕(1996-),女,硕士研究生,研究方向:机器视觉,E-mail:2391361235@qq.com。
