Image Detection Of Copper Plate Surface Nodule Defects Combined With Chaotic Bird Flock Threshold Segmentation

Copper electrolysis is an important process in copper smelting process.Due to the influence of various electrolytic process factors,nodular defects appear on the surface of cathode copper plate,which seriously affects its surface quality.Considering that in the process of manual identification of the defect,the interference of internal and external factors causes the operator to misjudge the result of the nodule on the surface of the copper plate,which affects the rationality of the final decision.Aiming at the above problems,this paper proposes a nodule defect image recognition scheme of copper plate combined with chaotic bird flock algorithm,aiming at improving the intelligence of enterprise production and reducing the production cost.The main work is as follows:(1)The difference of copper plate images collected from different perspectives and their influence on the detection accuracy of copper plate surface defects are analyzed.The matching feature points are located on the basis of considering the size of copper plate and the size of the collected images,and then the copper plate images are corrected by perspective transformation.(2)Aiming at the inherent defects of the traditional bird flock algorithm,this paper selects the individuals with the optimal location and the worst path in the population to carry out chaotic perturbation and position update with variable step size,so as to improve the probability of the algorithm jumping out of the local optimal position.Aiming at the lack of ergodic property of chaos theory,this paper improves the ergodic property of chaos theory and introduces the bird flock algorithm to enhance the global searching ability of the algorithm.Six test functions are used to test the proposed algorithm and its comparison algorithm,and the results show that the proposed algorithm has better performance.(3)KSW entropy is selected as the objective function of each algorithm,GA,CSO,FA,BSA and the proposed algorithm are used to pre-segment multiple copper plate images.In view of the influence of illumination and copper stripe on the segmentation effect of copper image,an 8-neighborhood search filter is proposed to correct the misclassified pixels,and a base point growth method is designed to remove the surface texture of copper image,so as to improve the detection accuracy of the algorithm for nodular defects on the surface of copper plate.The segmentation results are analyzed and compared under three indexes: time,fitness value and structural similarity(SSIM).The results show that the average fitness of the proposed algorithm can be increased by0.007 ? 1.707,and the SSIM value can be increased by 0.0034 ? 0.168.Finally,the quality of the copper surface is determined by calculating the pixel proportion of the copper image defect category.