Cathodic copper plate surface defect detection based on bird swarm algorithm with chaotic theory

Zhuo Wang; Changsheng Zhang; Wei Li; Junbing Qian; Duzuo Tang; Bing Cai; Yitao Chang

doi:10.11834/jig.190323

Image Analysis and Recognition | Views : 0 下载量: 70 CSCD: 1

PDF
Export
Share
Collection
Album

Cathodic copper plate surface defect detection based on bird swarm algorithm with chaotic theory
Vol. 25, Issue 4, Pages: 697-707(2020)
Received：12 July 2019，

Revised：2019-9-14，

Accepted：21 September 2019，

Published：16 April 2020
DOI： 10.11834/jig.190323
稿件说明：

移动端阅览

DOI：

Zhuo Wang, Changsheng Zhang, Wei Li, Junbing Qian, Duzuo Tang, Bing Cai, Yitao Chang. Cathodic copper plate surface defect detection based on bird swarm algorithm with chaotic theory[J]. Journal of Image and Graphics, 2020, 25(4): 697-707. DOI： 10.11834/jig.190323.

摘要

目的

铜电解过程中常因电解液溶解气体过饱阻止铜离子析出而在铜板表面形成凸起，常由操作员目视对铜板表面质量进行鉴别以决定归类，针对人工判别电解阴极铜板表面质量准确度和效率都较低的问题，提出一种结合混沌鸟群算法的铜板表面凸起智能识别方法。

方法

为增强算法的全局搜索能力，引入鸟群算法；选取鸟群劣质个体交替进行混和动态步长位置更新增加种群多样性以免陷入局部最优；对铜板表面缺陷进行分析，提出基点生长法并结合形态学开操作消除铜板图像纹理以提高算法对凸起面积计算的准确性。将最佳熵阈值确定法（Kapur-Sahoo-Wong，KSW）作为鸟群算法的适应度函数对铜板图像进行阈值分割，通过统计分割图像凸起像素点个数，得到实际凸起面积占比以决定铜板是否合格。

结果

将本文算法与遗传算法（genetic algorithm，GA）、鸡群算法（chicken swarm optimization，CSO）、萤火虫算法（glowworm swarm optimization，GSO）及鸟群算法（bird swarm algorithm，BSA）4种算法分别在时间、适应度值和结构相似度（structural similarity index measurement，SSIM）3个指标下分析对比，实验结果表明，本文算法适应度值可提高0.0030.701，SSIM值可提高0.0750.169。

结论

本文方法能有效检测铜板表面凸起面积占比并对其进行合格品、次品分类。

Abstract

Objective

From the global copper demand area

China still occupies the world's largest consumer of copper

accounting for nearly 40% of global total demand. Chinese economic data and news have been the focus of the copper market. In recent years

China's copper processing industry has shown a steady growth

and industry output gradually expanded. Copper processing materials are widely used in almost all national economy industries. Electrolytic copper plays an important role in copper smelting industry. During copper electrolysis

a large amount of air mixed in electrolyte is the main reason for the pores on the surface of copper cathode. The electrolyte dissolved gas is oversaturated to produce gas phase core

which gradually grows to form bubbles

and attaches to the surface of the cathode copper plate to form insulation point. This condition hinders the precipitation of copper ions

resulting in the appearance of bumps on the surface quality of copper plate. The surface quality of the copper plate is usually identified by the operator's eyes to determine the classification to solve the problem of low accuracy and efficiency of manually discriminating the surface quality of electrolytic cathode copper plate. This study proposes an intelligent recognition method of copper plate surface-raised image based on chaotic bird swarm algorithm.

Method

To enhance the global searching ability of the algorithm

the chaos theory is improved to make it enter into a completely chaotic state. The improved chaos theory was introduced based on the local optimal judgment of birds' own fitness value. In the iteration of bird flock

low-quality individuals were selected alternately to carry out chaos and dynamic step position update to increase the population diversity to avoid local optimization of the algorithm. Based on the analysis of surface defects of the copper plate

the base point growth method is proposed to design and detect structural elements. In addition

the texture of copper plate image is eliminated by combining with the morphological operation to improve the algorithm accuracy in calculating the raised area. The birds used Kapur-Sahoo-Wong(KSW) entropy as the fitness function to segment the copper plate images at the best threshold. Background pixels are mixed with raised category pixels in the initial segmentation result due to uneven illumination. Therefore

this study classifies the pixel of the corrected copper plate image through eight neighborhood search and takes it as the final segmentation result. According to the statistics of the number of raised pixels in the segmented image

the actual raised area proportion is obtained to determine whether the copper plate is qualified or defective. In this work

three copper plates with the smallest raised area are selected from 30 copper plates with fixed defective products through rough recognition by the human eyes. The exact bulge ratio is calculated using the algorithm in this study. To strengthen the fault tolerance rate of the algorithm

appropriate adjustment was made on the basis of the minimum value as the final threshold of copper plate classification. In addition

two arbitrary copper plates were selected to verify the method.

Result

The algorithm in this study is compared with genetic algorithm(GA)

chicken swarm optimization(CSO)

glowworm swarm optimization(GSO)

and bird swarm algorithm(BSA) in three indexes of time

fitness value

and structural similarity index measurement(SSIM) value

respectively. Experimental results show that the fitness value of the algorithm in this study was increased by 0.0030.701

and the SSIM value was increased by 0.0750.169.The finding reveals the fault tolerance and superiority of this algorithm in the calculation of the proportion of copper plate surface bulge.

Conclusion

The proposed method can effectively detect the proportion of copper surface bulge area and classify qualified and defective products

thus providing a reference to determine the surface quality of electrolytic copper in metallurgical industry.

关键词

Keywords

references

Chen Y G, Li L X, Peng H P, Xiao J H, Yang Y X and Shi Y H.2017. Particle swarm optimizer with two differential mutation. Applied Soft Computing, 61:314-330[DOI:10.1016/j.asoc.2017.07.020]

Ding T, Chang L, Li C S, Feng C and Zhang N. 2018. A mixed-strategy-based whale optimization algorithm for parameter identification of hydraulic turbine governing systems with a delayed water hammer effect. Energies, 11(9):2367[DOI:10.3390/en11092367]

Harrison K R, Engelbrecht A P and Ombuki-Berman B M. 2018a. Self-adaptive particle swarm optimization:a review and analysis of convergence. Swarm Intelligence, 12(3):187-226[DOI:10.1007/s11721-017-0150-9]

Harrison K R, Engelbrecht A P and Ombuki-Berman B M. 2018b. Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm and Evolutionary Computation, 41:20-35[DOI:10.1016/j.swevo.2018.01.006]

He Y C, Xie H R, Wong T L and Wang X Z. 2018. A novel binary artificial bee colony algorithm for the set-union knapsack problem. Future Generation Computer Systems, 78:77-86[DOI:10.1016/j.future.2017.05.044]

Holland J H. 1973. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2):88-105[DOI:10.1137/0202009]

Kiran M S. 2017. Particle swarm optimization with a new update mechanism. Applied Soft Computing, 60:670-678[DOI:10.1016/j.asoc.2017.07.050]

Krishnanand K N and Ghose D. 2009. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence, 3(2):87-124[DOI:10.1007/s11721-008-0021-5]

Lakshminarayanan S and Kaur D. 2018. Optimal maintenance scheduling of generator units using discrete integer cuckoo search optimization algorithm. Swarm and Evolutionary Computation, 42:89-98[DOI:10.1016/j.swevo.2018.02.016]

Liu D, Zhang X Y and Chen Y J. 2016. Monocrystalline silicon diameter detection image threshold segmentation method using multi-objective artificial fish swarm algorithm. Acta Automatica Sinica, 42(3):431-442

刘丁, 张新雨, 陈亚军. 2016.基于多目标人工鱼群算法的硅单晶直径检测图像阈值分割方法.自动化学报, 42(3):431-442)[DOI:10.16383/j.aas.2016.c150587]

Majumder A, Laha D and Suganthan P N. 2018. A hybrid cuckoo search algorithm in parallel batch processing machines with unequal job ready times. Computers and Industrial Engineering, 124:65-76[DOI:10.1016/j.cie.2018.07.001]

Makas H and Yumuşak N. 2016. System identification by using migrating birds optimization algorithm:a comparative performance analysis. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3):1879-1900[DOI:10.3906/elk-1311-45]

Manshahia M S. 2018. Swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities. IET Wireless Sensor Systems, 8(6):256-259[DOI:10.1049/iet-wss.2018.5143]

Meng X B, Gao X Z, Lu L H, Liu Y and Zhang H Z. 2016. A new bio-inspired optimisation algorithm:bird swarm algorithm. Journal of Experimental and Theoretical Artificial Intelligence, 28(4):673-687[DOI:10.1080/0952813X.2015.1042530]

Meng X B, Liu Y, Gao X Z and Zhang H Z. 2014. A new bio-inspired algorithm:chicken swarm optimization. Lecture Notes in Computer Science, 8794(1):86-94[DOI:10.1007/978-3-319-11857-4_10]

Mohapatra P, Das K N and Roy S. 2017. A modified competitive swarm optimizer for large scale optimization problems. Applied Soft Computing, 59:340-362[DOI:10.1016/j.asoc.2017.05.060]

Ren C L, Zhang C Y, Meng L L, Yu J and Hong H. 2019. Hybrid flow-shop scheduling problems based on improved migrating birds optimization algorithm. Computer Integrated Manufacturing Systems, 25(3):643-653

任彩乐, 张超勇, 孟磊磊, 余俊, 洪辉. 2019.基于改进候鸟优化算法的混合流水车间调度问题.计算机集成制造系统, 25(3):643-653)[DOI:10.13196/j.cims.2019.03.011]

Sun H, Zhang X S, Chen J W, Guo Y and Wei M T. 2018. Optimization of electronic prescription for parallel external fixator based on ant colony algorithm. Chinese Journal of Scientific Instrument, 39(10):232-240

孙昊, 张习帅, 陈建文, 郭悦, 魏梦婷. 2018.基于蚁群算法的骨外固定器处方优化.仪器仪表学报, 39(10):232-240)[DOI:10.19650/j.cnki.cjsi.J1803798]

Wang D S, Tan D P and Liu L. 2018. Particle swarm optimization algorithm:an overview. Soft Computing, 22(2):387-408[DOI:10.1007/s00500-016-2474-6]

Wang H J, Yang W R and Yang Q X. 2019. Application of improved bird swarm algorithm in home appliance load disaggregation. Proceedings of the CSU-EPSA, 31(10):140-144.

王慧娟, 杨文荣, 杨庆新. 2019.改进鸟群算法在家电负荷分解中的应用.电力系统及其自动化学报, 31(10):140-144.)[DOI:10.19635/j.cnki.csu-epsa.000165]

Wang J W and Peng Y G. 2018. Improved bird swarm algorithm based on migration and mutation strategy and its application in parameter estimation. Journal of East China University of Science and Technology (Natural Science Edition), 44(4):617-624

王建伟, 彭亦功. 2018.引入迁移和变异策略的改进鸟群算法及其在参数估计中的应用.华东理工大学学报(自然科学版), 44(4):617-624)[DOI:10.14135/j.cnki.1006-3080.20170709001]

Wang X H, Deng Y M and Duan H B. 2018. Edge-based target detection for unmanned aerial vehicles using competitive bird swarm algorithm. Aerospace Science and Technology, 78:708-720[DOI:10.1016/j.ast.2018.04.047]

Alert me when the article has been cited

提交

Airport runway detection based on LBP feature classification in PolSAR images

Stereoscopic Video Object Extraction Based on Disparity and Threshold

Improved 2D Otsu Algorithm for SAR Images

Automatic detection of hard exudatesbased on RBF neural network and threshold segmentation

Three-dimensional adaptive minimum error thresholding segmentation algorithm