|本期目录/Table of Contents|

[1]滕 腾,樊春玲,张春堂.基于机器视觉的托盘生产线上原料木板的识别[J].工业仪表与自动化装置,2022,(02):67-71+125.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.014]
 TENG Teng,FAN Chunling,ZHANG Chuntang.Identification of raw board in pallet production line based on machine vision[J].Industrial Instrumentation & Automation,2022,(02):67-71+125.[doi:10.19950/j.cnki.cn61-1121/th.2022.02.014]
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基于机器视觉的托盘生产线上原料木板的识别

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年02期
页码:
67-71+125
栏目:
出版日期:
2022-04-15

文章信息/Info

Title:
Identification of raw board in pallet production line based on machine vision
文章编号:
1000-0682(2022)02-0000-00
作者:
滕 腾樊春玲张春堂
(青岛科技大学 自动化与电子工程学院,山东 青岛 266100)
Author(s):
TENG TengFAN ChunlingZHANG Chuntang
(College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Shandong Qingdao 266000, China)
关键词:
机器视觉骨架提取托盘生产自动上料
Keywords:
machine vision skeleton extraction pallet production automatic loading
分类号:
TP29;TP751.1
DOI:
10.19950/j.cnki.cn61-1121/th.2022.02.014
文献标志码:
A
摘要:
传统木制托盘生产的上料方式多为人工徒手上料,不仅效率低下,而且托盘生产时的巨大噪音还会危害上料工人健康。为了提高木制托盘的生产效率,保护上料工人,该文提出一种基于机器视觉的托盘生产线上原料木板的识别算法。首先,对获取的图片进行图像预处理,提高信噪比,方便后续的特征提取;其次,使用形态学细化(骨架提取)算法配合八邻域定位算法获取离散的特征点信息;之后,使用特征点匹配算法让原料木板与特征点相互对应;最后,利用木板尺寸信息与木板特征长度构建分类器对原料木板进行类型判断,同时根据特征点计算木板位置坐标。实验表明,这套原料木板识别算法简单有效,识别准确率达到96%,满足托盘生产线的上料需要。
Abstract:
The loading method of traditional wooden pallet production is mostly inefficient manual loading. When using this feeding method, the huge noise during pallet production endangers the health of the feeding. In order to improve the production efficiency of wooden pallets and protect the loading workers, this paper proposes a raw board identification algorithm based on machine vision. First, we perform image preprocessing on the board pictures to improve the signal-to-noise ratio. Then, we employ the morphological refinement (skeleton extraction) algorithm and the eight-neighbor positioning algorithm to obtain discrete feature point information, matching algorithm matching the raw board and the feature points through the feature point; Finally, we construct a classifier by combining the size information and the feature length to identify the type of the board, calculating the center of mass coordinates of the board according. Experiments show that the identification accuracy of the raw board recognition algorithm reaches 96%, which meets the loading needs of pallet production line.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-11-03

作者简介:
滕腾(1996),男,山东省济宁人,硕士研究生,研究方向为机器视觉和信息检测与处理。
更新日期/Last Update: 1900-01-01