[1]李 威,李 楠,于清玲.基于 Faster RCNN 的可回收物自动分类算法研究[J].计算机技术与发展,2021,31(增刊):100-105.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 020]
　LI Wei,LI Nan,YU Qing-ling.Research on Automatic Classification Algorithm of Recyclables Based on Faster RCNN[J].,2021,31(增刊):100-105.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 020]
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基于 Faster RCNN 的可回收物自动分类算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年增刊
页码:
100-105
栏目:
应用前沿与综合
出版日期:
2021-12-31

文章信息/Info

Title:
Research on Automatic Classification Algorithm of Recyclables Based on Faster RCNN
文章编号:
1673-629X(2021)S0100-06
作者:
李 威; 李 楠; 于清玲
沈阳工业大学 信息科学与工程学院,辽宁 沈阳 110870
Author(s):
LI Wei; LI Nan; YU Qing-ling
School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China
关键词:
垃圾分类; 深度学习; 目标检测; Faster RCNN; 区域建议网络; 锚框
Keywords:
classification; deep learning; target detection; Faster RCNN; region proposal network; anchor
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2021. S. 020
摘要:
利用机器视觉对可回收物进行自动分类识别可以提高废品回收再利用的效率。 应用改进的 Faster RCNN 算法对办公区四种常见的可回收物,塑料瓶、易拉罐、玻璃瓶和纸张进行自动分类识别。 通过对特征提取网络和区域建议网络进行改进,提升 Faster RCNN 算法的检测精度。 对第三方专业数据平台科赛网提供的可回收物图像库和自采图像库分别进行训练和测试。 对存在相似特征的玻璃瓶和塑料瓶误检率较高问题,引入待检目标的重量特征,提高了对二者的召回率。实验结果表明,结合重量特征的改进 Faster RCNN 算法在四类可回收物识别精度上分别为 92. 28% 、87. 78% 、89. 25% 和91. 24% ,平均检测时间为 0. 202 秒,提升了可回收物自动分类识别的效率。
Abstract:
Using machine vision to automatically classify and identify recyclables can improve the efficiency of waste recycling. Using the improved Faster RCNN algorithm to automatically classify and identify four common recyclables in the office area,plastic bottles,cans,glass bottles and paper. Improve the detection accuracy of Faster RCNN algorithm by improving the feature extraction network and regionproposal network. Train and test the recyclables image library provided by the third - party professional data platform Kesai. com,and train and test self - collected image library. For glass bottles and plastic bottles with similar characteristics, the false detection rate is relatively high. The introduction of the weight characteristics of the target to be inspected improves the recall rate of both. The experimental results show that the improved Faster RCNN algorithm combined with weight characteristics has 92. 28% ,87. 78% ,89.25% and 91. 24% in the recognition accuracy of the four types of recyclables,and the average detection time is 0. 202 seconds,which improves the efficiency of automatic classification and recognition of recyclables.

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更新日期/Last Update: 2021-09-10