[1]戚云涛,曾寿金,方宇轩,等.基于粒子群优化的双凸透镜缺陷Ostu阈值分割算法[J].福建理工大学学报,2023,21(06):598-604.[doi:10.3969/j.issn.1672-4348.2023.06.014]
 QI Yuntao,ZENG Shoujin,FANG Yuxuan,et al.Otsu threshold segmentation algorithm for defects ofdouble convex lens based on particle swarm optimization[J].Journal of Fujian University of Technology;,2023,21(06):598-604.[doi:10.3969/j.issn.1672-4348.2023.06.014]
点击复制

基于粒子群优化的双凸透镜缺陷Ostu阈值分割算法()
分享到:

《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第21卷
期数:
2023年06期
页码:
598-604
栏目:
出版日期:
2023-12-25

文章信息/Info

Title:
Otsu threshold segmentation algorithm for defects ofdouble convex lens based on particle swarm optimization
作者:
戚云涛曾寿金方宇轩蔡晓洁沈庆梅
福建理工大学机械与汽车工程学院
Author(s):
QI Yuntao ZENG Shoujin FANG Yuxuan CAI Xiaojie SHEN Qingmei
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
数字图像处理缺陷检测粒子群优化双阈值图像分割
Keywords:
digital image processingdefect detectionparticle swarm optimizationdouble thresholdimage segmentation
分类号:
TP391
DOI:
10.3969/j.issn.1672-4348.2023.06.014
文献标志码:
A
摘要:
为提高双凸透镜在实时缺陷检测时阈值分割的速度和精度,提出了改进粒子群优化算法(particle swarm optimization, PSO)优化Otsu 双阈值分割(Otsu??s thresholding method)。通过改进粒子群算法的权重函数并引入约束因子增强粒子前期全局搜索能力,提升了后期局部收敛速度;在判断是否陷入局部最优时加入扰动,防止粒子后期陷入局部最优;用粒子当前位置替换全局位置时,为减少粒子资源浪费,通过重新分配位置和速度激活粒子搜索能力,提升了整体粒子群的全局搜索能力。实验证明,采用改进的PSO 算法对图像进行双阈值分割,比Otsu 双阈值分割节省约52.7%的时间,比PSO+Otsu 算法节省约32.3%的时间,而且其阈值分割的精度也得到了提升。
Abstract:
In order to improve the speed and accuracy of threshold segmentation in real-time defect detection of double convex lens, an improved particle swarm optimization (PSO) algorithm was proposed to optimize Otsus thresholding method. By improving the weight function of PSO and introducing constraint factors, the global search ability of particles in the early stage was enhanced, and the local convergence speed in the later stage was improved. Perturbation was added to determine whether the particles fall into local optimum, which prevented the particles from doing so in the later stage. When the global position was replaced by the current position of the particle, in order to reduce the waste of particle resources, the particle search capability was activated by reassigning the position and speed, and the global search capability of the overall particle swarm was improved. Experiments show that the improved PSO algorithm can save about 52.7% of the time compared with Otsu double threshold segmentation and about 32.3% of the time compared with PSO+Otsu algorithm, and the accuracy of the threshold segmentation is also improved.

参考文献/References:

[1] 朱宇栋,陈於学. 光学镜片外观瑕疵视觉检测方法[J]. 应用光学,2020,41(3):553-558.[2] 国家质量监督检验检疫总局,中国国家标准化管理委员会. 光学零件表面疵病:GB/T 1185—2006[S]. 北京:中国标准出版社,2007.[3] 姚红兵,曾祥波,马桂殿,等. 镜片疵病视觉在线检测方法[J]. 激光与光电子学进展,2013,50(12):77-82.[4] 赵朗月,吴一全. 基于机器视觉的表面缺陷检测方法研究进展[J]. 仪器仪表学报,2022,43(1):198-219.[5] 刘硕. 阈值分割技术发展现状综述[J]. 科技创新与应用,2020(24):129-130.[6] 刘丽霞,李宝文,王阳萍,等. 改进Canny边缘检测的遥感影像分割[J]. 计算机工程与应用,2019,55(12):54-58,180.[7] 汪文琪,李宗春,付永健,等. 基于改进多规则区域生长的点云多要素分割[J]. 光学学报,2021,41(5):198-212.[8] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems,Man,and Cybernetics,1979,9(1):62-66.[9] 郭宗建,邓绍强,汤可宗. 一种自适应粒子群优化算法在陶瓷图像分割中的应用[J]. 陶瓷学报,2022,43(3):478-484.[10] SONG Y J,LIU Y,CHEN H Y,et al. A multi-strategy adaptive particle swarm optimization algorithm for solving optimization problem[J]. Electronics,2023,12(3):491.[11] 王树亮,赵合计. 基于改进粒子群算法的多阈值灰度图像分割[J]. 计算机应用,2012,32(S2):147-150.[12] 周文峰,梁晓磊,唐可心,等. 具有拓扑时变和搜索扰动的混合粒子群优化算法[J]. 计算机应用,2020,40(7):1913-1918.[13] 沈夏炯,段晓宇,原万里,等. 基于连通区域标记算法的圆检测算法的研究[J]. 计算机工程与应用,2018,54(21):95-98,106.[14] 曹宇,徐传鹏. 一种改进阈值分割算法在镜片缺陷检测中的应用[J]. 激光与光电子学进展,2021,58(16):219-224.

相似文献/References:

[1]檀甫贵、邹复民、刘丽桑、李建兴.基于机器视觉的软包锂电池表面缺陷检测[J].福建理工大学学报,2020,18(03):267.[doi:10.3969/j.issn.1672-4348.2020.03.012]
 TAN Fugui,ZOU Fumin,LIU Lisang,et al.Surface defect detection of soft-pack lithium battery based on machine vision[J].Journal of Fujian University of Technology;,2020,18(06):267.[doi:10.3969/j.issn.1672-4348.2020.03.012]
[2]叶建华,唐辉,罗奋翔,等.基于改进MobileNet的咖啡豆缺陷检测[J].福建理工大学学报,2023,21(03):257.[doi:10.3969/j.issn.1672-4348.2023.03.009]
 YE Jianhua,TANG Hui,LUO Fenxiang,et al.Coffee bean defect detection based on improved MobileNet[J].Journal of Fujian University of Technology;,2023,21(06):257.[doi:10.3969/j.issn.1672-4348.2023.03.009]
[3]于明源,周景亮,曾绍锋,等.基于YOLOv8改进的轴承表面缺陷检测方法[J].福建理工大学学报,2024,22(03):280.[doi:10.3969/j.issn.2097-3853.2024.03.011]
 YU Mingyuan,ZHOU Jingliang,ZENG Shaofeng,et al.Improved bearing surface defect detection method based on YOLOv8[J].Journal of Fujian University of Technology;,2024,22(06):280.[doi:10.3969/j.issn.2097-3853.2024.03.011]

更新日期/Last Update: 2023-12-25