Defect detection method of carton based on block feature matching

XIAO Bin; YANG Tao; MEI Yanying

doi:10.13873/J.1000-9787(2020)09-0138-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Defect detection method of carton based on block feature matching

更新时间：2025-09-11

- Defect detection method of carton based on block feature matching
- Vol. 39, Issue 9, Pages: 138-141(2020)
- 作者机构：
  
  1. 西南科技大学信息工程学院
  2. 特殊环境机器人四川省重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)09-0138-04
  CLC： TS77;TP391.41
- Published：2020
- 稿件说明：
移动端阅览
[1]肖斌,杨涛,梅艳莹.基于分块特征匹配的纸箱缺陷检测方法[J].传感器与微系统,2020,39(09):138-141.

XIAO Bin, YANG Tao, MEI Yanying. Defect detection method of carton based on block feature matching[J]. 2020, 39(9): 138-141.
[1]肖斌,杨涛,梅艳莹.基于分块特征匹配的纸箱缺陷检测方法[J].传感器与微系统,2020,39(09):138-141. DOI： 10.13873/J.1000-9787(2020)09-0138-04.

XIAO Bin, YANG Tao, MEI Yanying. Defect detection method of carton based on block feature matching[J]. 2020, 39(9): 138-141. DOI： 10.13873/J.1000-9787(2020)09-0138-04.

摘要

针对物流运输中产生的纸箱缺陷问题

提出一种基于分块特征匹配的纸箱缺陷检测方法。该方法通过将待测纸箱图像与已知图案特征匹配

实现纸箱图案与非图案区域分离;通过Canny边缘检测算法结合图像的矩来计算非图案区域的边缘轮廓面积

判断是否存在缺陷;通过改进的ORB算法结合网格运动统计方法

将图案区域与已知图案的特征点进行匹配并计算匹配率

判定是否存在缺陷。实验表明:该方法检测纸箱图案与非图案区域缺陷的准确率分别为91.5%与93.5%

具有较高的识别率。

Abstract

A carton defect detection method based on block feature matching is proposed

aiming at the problem of carton defects in transportation. In this method

the carton image to be tested is matched with the known pattern feature to accomplish the separation of the patterned and nonpatterned area of the carton image. The canny edge detection algorithm is combined with the image moment to calculate the edge contour area to identify the defect in the nonpatterned area. The pattern area to be tested is matched with the feature point of known patterns with the improved ORB algorithm combined with the grid motion statistics method

and the matching rate is calculated to identify the defects. The experimental results show that the accuracy of the proposed method is 91. 5 % and93. 5 % respectively

which has high identification rate.

关键词

Keywords

references

基于深度学习的工业自动化包装缺陷检测方法 [J]. 李建明,杨挺,王惠栋. 包装工程 . 2020(07)

基于网格运动统计的自适应图像特征匹配算法 [J]. 柳长安,艾壮,赵丽娟. 华中科技大学学报(自然科学版) . 2020(01)

基于改进网格运动统计特征的图像匹配算法 [J]. 朱成德,李志伟,王凯,高燕,郭亨长. 计算机应用 . 2019(08)

基于改进梯度幅值的包装缺陷检测算法研究及应用 [J]. 宋丽梅,徐婧玮,杨燕罡,郭庆华,杨怀栋. 应用光学 . 2019(04)

基于改进ORB和网格运动统计的图像匹配算法 [J]. 罗守品,杨涛,梅艳莹. 传感器与微系统 . 2019(07)

基于SURF和RANSAC算法的同步相移干涉图位置配准方法 [J]. 靳京京,卢文龙,郭小庭,刘晓军. 光学学报 . 2017(10)

基于图像配准的食品包装印刷缺陷检测与实现 [J]. 杨祖彬,代小红. 计算机科学 . 2015(08)

基于图像识别技术的烟包封条缺陷检测 [J]. 赵众,常灿,陈磊. 北京工业大学学报 . 2014(07)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Image matching algorithm based on improved ORB and grid-based motion statistics

MIM gear defect detection based on transfer learning and feature reuse

Application of ORB algorithm based on K-means in recognition of front vehicle

Reflective arc surface defect detection based on template matching and deep learning

Optimization of planar array capacitive sensor

Related Author

MEI Yanying

YANG Tao

LUO Shoupin

QI Guanghao

ZHAO Tong

LEI Baozhen

WANG Xunwei

XU Yurui

Related Institution

Key Laboratory of Sichuan Province for Robot Technology Used for Special Environment

School of Information Engineering,Southwest University of Science＆Technology

Beijing Intelligent Machinery Innovation Design Service Engineering Technology Research Center,Beijing Union University

Beijing Key Laboratory of Information Service Engineering, Beijing Union University

Institute For Future, Qingdao University

⁰