基于嵌入式平台和轻量化模型的板材计数装置  

Stacked plate counting instrument based on embedded platform with a lightweight model

作　　者：刘忠英 翟鹏飞 侯维岩[1] Liu Zhongying;Zhai Pengfei;Hou Weiyan(School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学电气与信息工程学院,郑州450001

出　　处：《电子测量技术》2024年第9期46-51,共6页Electronic Measurement Technology

基　　金：国家自然科学基金委重大研究计划(9206710030)项目资助。

摘　　要：针对堆叠板材计数过程中人工计数法效率低、准确性不高的问题。本文提出了一套基于嵌入式平台和轻量化模型的板材计数装置,将改进的Faster R-CNN网络植入工控机中运行,可以在工业和物流现场实时识别板材的数量。内置网络使用轻量级网络MobileNetv2融合轻量通道注意力机制ECA作为骨干网络,使用空间注意力机制和倒置残差结构重构FPN架构,并提出了一种基于高度交并比的HIOU_Loc预测框去冗余处理新算法,以缓解小目标检测困难的难题。在基于N4100平台的工控机中运行实验表明:本文所提出的算法对板材计数准确度达到了98.51%,检测一张高分辨率板材图像仅需0.31 s。本装置设计了一个校正模块,经过人工后处理后,对于堆叠板材的计数准确率可以达到100%,满足了实际场景下对板材实时计量的需求。Stacked plate are counted by hand,which takes long time and has poor accuracy.Hence,the paper proposes a plate counting instrument based on embedded platform with a lightweight model.The instrument can detect in real time the number of stacked plate at production and logistics site,which deploys the improved Faster R-CNN network to the Industrial Personal Computer.In order to alleviate the difficulty of small object detection,the network algorithm by using lightweight network MobileNetv2 to integrate the efficient channel attention as the backbone network,using spatial attention and inverted residual structure module to reconstruct the FPN structure,proposing an HIOU_Loc algorithm based on on Height intersection over union to remove redundant prediction boxes.The plate counting experiment on a IPC equipped with N4100 CPU.The results show that the accuracy of the plate counting algorithm proposed in this paper reaches 98.51%,and it only takes 0.31 s to detect a high-resolution plate image.A quantitative calibration module is designed for the instrument.The instrument can reach 100%accuracy in counting stacked plate after the manual calibration module,which meets the requirements of stacked plate real-time counting in practical scenarios.

关 键 词：堆叠板材计数装置 Faster R-CNN 轻量化卷积神经网络 K-means++ 小目标检测 

分 类 号：TN249.2[电子电信—物理电子学] TP391.41[自动化与计算机技术—计算机应用技术]