[1]王 飞,陈亮杰,王 梨,等.基于卷积神经网络的仓储物体检测算法研究[J].南京师范大学学报(工程技术版),2019,19(04):099-105.[doi:10.3969/j.issn.1672-1292.2019.04.017]
 Wang Fei,Chen Liangjie,Wang Li,et al.Research on Warehouse Object Detection AlgorithmBased on Convolutional Neural Network[J].Journal of Nanjing Normal University(Engineering and Technology),2019,19(04):099-105.[doi:10.3969/j.issn.1672-1292.2019.04.017]
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基于卷积神经网络的仓储物体检测算法研究
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
19卷
期数:
2019年04期
页码:
099-105
栏目:
计算机工程
出版日期:
2019-12-31

文章信息/Info

Title:
Research on Warehouse Object Detection AlgorithmBased on Convolutional Neural Network
文章编号:
1672-1292(2019)04-0099-07
作者:
王 飞1陈亮杰2王 梨2王 林2
(1.贵州民族大学人文科技学院,贵州 贵阳 550025)(2.贵州民族大学数据科学与信息工程学院,贵州 贵阳 550025)
Author(s):
Wang Fei1Chen Liangjie2Wang Li2Wang Lin2
(1.College of Humanities & Sciences of Guizhou Minzu University,Guiyang 550025,China)(2.College of Data Science and Information Engineering,Guizhou Minzu University,Guiyang 550025,China)
关键词:
卷积神经网络仓储环境物体检测DSOD
Keywords:
convolutional neural networkwarehouse environmentobject detectiondeeply supervised object detectors(DSOD)
分类号:
TP391.41
DOI:
10.3969/j.issn.1672-1292.2019.04.017
文献标志码:
A
摘要:
针对仓储环境中物体检测公开数据集匮乏的问题,通过摄像机采集真实仓储环境中包含货物、托盘和叉车的大量图像进行标注,创建了一个仓储物体数据集. 同时针对传统物体检测算法在仓储环境中检测准确率较低的问题,将基于卷积神经网络的DSOD应用于仓储环境中,通过在自己创建的仓储物体数据集上从零开始训练DSOD模型,实现了仓储物体的准确性检测. 该算法的mAP达到了93.81%,比Faster R-CNN、SSD分别提高了0.04%、1.44%; 并且模型大小仅有51.3 MB,比Faster R-CNN、SSD分别减小了184.5 MB、43.4 MB. 实验结果表明,该算法获得了较为满意的仓储物体检测效果,其在仓储物体检测领域具有一定的实用价值.
Abstract:
Considering the lack of public datasets for object detection based on the warehouse environment,a large number of images containing cargos,trays and forklifts in real warehouse environment are collected and labeled to build the warehouse object dataset. Meanwhile,aiming at the problem that the traditional object detection algorithm has lower detection accuracy in warehouse environment,the deeply supervised object detectors(DSOD)based on convolutional neural network is applied to the warehouse environment,and the DSOD model is trained from scratch on the self-built warehouse object dataset,and the accuracy detection of the warehouse object is realized. The mean Average Precision(mAP)of this algorithm reaches 93.81%,which is higher than that of Faster R-CNN and SSD by 0.04 and 1.44 points respectively,and the model size of this algorithm is only 51.3 MB,which is lower than that of Faster R-CNN and SSD by 184.5 MB and 43.4 MB respectively. The experimental results show that the algorithm has a relatively satisfying warehouse object detection effect,and it has certain practical values in the field of warehouse object detection.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-07-05.
基金项目:贵州省教育厅创新群体重大研究项目(黔教合KY字[2018]018)、贵州省科技厅重点实验室(黔科合计Z字[2009]4002)、贵州民族大学人文科技学院基金科研项目(18rwjs016).
通讯联系人:王飞,助教,研究方向:图像处理、模式识别. E-mail:wangfei10248@163.com
更新日期/Last Update: 2019-12-31