摘要
为提高医用塑瓶包装生产线上装箱计数的效率、准确率及稳定性,本文提出一种基于深度学习的装箱计数检测算法,实现在线实时计数。首先,构建以ResNet为骨架网络,使用特征金字塔网络产生多尺度特征图并适当删减卷积层的改进RetinaNet网络。然后,使用聚类算法优化Anchor尺寸,使算法能够自适应歪瓶、倒瓶等异常情况下的计数检测,从而降低漏检率并提高定位精度。最后,在实际装箱数据集上对算法进行实验评测,结果表明该算法抗干扰能力强、稳健可靠,在满足生产条件下能够快速、准确地对装箱塑瓶进行计数检测,计数精度可达99.98%以上,单张检测时间为33 ms,满足了生产线实时检测要求。
In order to improve the efficiency,accuracy and stability of packing counting in medical plastic packaging production line,this paper proposes a packing counting detection algorithm based on deep learning which can realize automatic online counting.Firstly,an improved RetinaNet network was constructed with ResNet as the framework,the feature pyramid network was used to generate multi-scale feature maps,and the convolution layers were cut appropriately.Then,clustering algorithm is used to optimize the Anchor size,so that the algorithm can adapt to count detection under abnormal conditions such as crooked bottle and inverted bottle,so as to reduce the missed detection rate and improve the positioning accuracy.Finally,the experimental evaluation of the algorithm on the actual packing data set shows that the algorithm is robust and reliable,and can quickly and accurately count and detect the packing plastic bottles under the production conditions.The counting accuracy can reach more than 99.98%,and the single detection time is 33 ms,which meets the real-time detection requirements of the production line.
作者
邱吕
任德均
郜明
付磊
吴华运
胡云起
QIU Lyu;REN De-jun;GAO Ming;FU Lei;WU Hua-yun;HU Yun-qi(School of Mechanical Engineering,Sichuan University,Chengdu 610065,China)
出处
《计算机与现代化》
2020年第12期99-103,111,共6页
Computer and Modernization
