基于深度学习的嵌入式汽车内饰件装配检测  

Embedded Automotive Interior Parts Assembly Inspection Based on Deep Learning

作　　者：谭任 唐忠 王鸿亮 王帅[2] TAN Ren;TANG Zhong;WANG Hong-Liang;WANG Shuai(College of Computer Science and Technology,Shenyang University of Chemical Technology,Shenyang 110027,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)

机构地区：[1]沈阳化工大学计算机科学与技术学院,沈阳110027 [2]中国科学院大学,北京100049 [3]中国科学院沈阳计算技术研究所,沈阳110168

出　　处：《计算机系统应用》2022年第4期110-116,共7页Computer Systems & Applications

基　　金：沈阳市重大科技成果转化专项(20-203-5-40);辽宁省工业重大专项(2019030151-JH1/101)。

摘　　要：汽车内饰件装配后的质量检测是装配的重要阶段,是确保内饰件装配高通过率的重要保障.以低功耗高性能英伟达的开发板搭建目标检测硬件平台,对比Faster RCNN与YOLOv5模型,采用对小目标检测效果更好的YOLOv5模型训练工业摄像头采集的数据.试验结果表明,对汽车内饰装配件13种特征检测的准确率都高达95%以上,实现了对汽车内饰装配件高效精准的判别,为汽车内饰件的装配工作提供了可靠的辅助手段.The quality inspection after assembly of automotive interior parts is an important stage of assembly and an important guarantee for ensuring a high pass rate of interior parts assembly. The target detection hardware platform is built with low-power and high-performance NVIDIA development boards, and the Faster RCNN and YOLOv5 models are compared, and the YOLOv5 model, which has a better detection effect on small targets, is used to train the data collected by industrial cameras. The test results show that the accuracy of detecting 13 features of automobile interior fittings is as high as 95%, which realizes the efficient and accurate discrimination of automobile interior fittings and provides reliable auxiliary means for the assembly work of automobile interior fittings.

关 键 词：汽车内饰装配件 目标检测 YOLOv5 Faster RCNN 深度学习 检测方法 

分 类 号：U468[机械工程—车辆工程] TP18[交通运输工程—载运工具运用工程] TP391.41[交通运输工程—道路与铁道工程]