一种利用外轮廓配准的活塞侧面缺陷检测方法

doi:10.19665/j.issn1001-2400.2019.05.011

西安电子科技大学学报 ›› 2019, Vol. 46 ›› Issue (5): 75-83.doi: 10.19665/j.issn1001-2400.2019.05.011

一种利用外轮廓配准的活塞侧面缺陷检测方法

王红艳^1,^2,³,朱利民⁴,张潘杰^1,^2,³,李金屏^1,^2,³()

^1. 济南大学信息科学与工程学院,山东济南 250022
^2. 济南大学山东省网络环境智能计算技术重点实验室,山东济南 250022
^3. 济南大学山东省“十三五”高校信息处理与认知计算重点实验室,山东济南 250022
^4. 滨州渤海活塞有限公司,山东滨州 256602

收稿日期:2019-03-11 出版日期:2019-10-20 发布日期:2019-10-30
通讯作者: 李金屏
作者简介:王红艳(1995—),女,济南大学硕士研究生,E-mail:fairy_w@qq.com.
基金资助:
国家自然科学基金(61701192);山东省重点研发计划(2017CXGC0810);山东省科技重大专项新兴产业项目(2015ZDXX0801A03);山东省教育科学规划“教育招生考试科学研究专设课题”(ZK1337212B008)

Method for the detection of the piston side defect based on external contour registration

WANG Hongyan^1,^2,³,ZHU Limin⁴,ZHANG Panjie^1,^2,³,LI Jinping^1,^2,³()

^1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China
^2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
^3. Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in the 13th Five-Year Plan,University of Jinan, Jinan 250022, China
^4. Binzhou Bohai Piston Co., Ltd., Binzhou 256602, China

Received:2019-03-11 Online:2019-10-20 Published:2019-10-30
Contact: Jinping LI

摘要/Abstract

摘要：

为了有效地对活塞侧面进行缺陷检测,根据两个相同型号和规格的活塞在相同的光照及角度下拍摄得到的图像极其相似的特点,提出了基于外轮廓配准的活塞侧面缺陷检测方法。该方法分为四步:第一,在特定的光照条件下,对不同型号和规格的标准活塞进行多角度的图像采集,建立标准模板库;第二,利用尺度不变特征转换算法,根据外轮廓特征点对模板图像和待检测图像做图像配准,找到两幅图像相对应的区域;第三,用相同大小的滑动窗口分别对两幅图像的相对应区域进行遍历,提取窗口内的特征,包括灰度均值、灰度方差、垂直投影、水平投影;第四,通过比较两个窗口内的特征值,判断窗口内是否存在缺陷。结果表明,该方法能够有效地检测活塞表面缺陷及确定缺陷位置,准确率约为94.78%,具有较强的实用性。

关键词: 缺陷检测, 活塞表面, 特征提取, 图像配准

Abstract:

In order to detect the defects on the side of the piston, we propose an effective method for detecting the piston side defect based on the registration of piston contour according to the high similarity between the images of two pistons of the same type and specification under the same illumination and angle. The method is divided into four steps: first, we establish a standard template dataset by taking multi-angle images of standard pistons of different types and specifications under standard illumination conditions; second, we use the Scale Invariant Feature Transform (SIFT) algorithm to register the template image and the current image according to the piston contour features so as to find the exactly corresponding region of the two images; third, the corresponding regions of the two images are traversed with sliding windows of the same size to calculate such features as mean, variance, vertical projection and horizontal projection; finally, we determine whether there is a defect in the current window by comparing the features of two corresponding windows. The results show that the method can effectively detect the piston surface defect and determine the position of the defect, and that the accuracy rate is 94.78%, and it has strong practicability.

Key words: defect detection, piston surface, feature extraction, image registration

中图分类号:

TP391

王红艳,朱利民,张潘杰,李金屏. 一种利用外轮廓配准的活塞侧面缺陷检测方法[J]. 西安电子科技大学学报, 2019, 46(5): 75-83.

WANG Hongyan,ZHU Limin,ZHANG Panjie,LI Jinping. Method for the detection of the piston side defect based on external contour registration[J]. Journal of Xidian University, 2019, 46(5): 75-83.

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参考文献 18

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样本类型	待检测数目	缺陷图像数目	检测正确数目	漏报数目	误报数目
型号1	200	68	189	4	7
型号2	222	28	211	1	10
合计	422	96	400	5	17

缺陷类型	待检测数目	检测正确数目	漏报数目
斑点缺陷	24	22	2
划痕缺陷	59	58	1
缺口缺陷	13	11	2

一种利用外轮廓配准的活塞侧面缺陷检测方法

Method for the detection of the piston side defect based on external contour registration

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