Abstract
There are various mathematical optimization problems that can be effectively solved by meta-heuristic algorithms. The improvement of these algorithms is that they carry out iterative search processes which resourcefully act upon exploration and exploitation in spatial domain containing global and local optima. An innovative robust Cuckoo Optimization Algorithm (COA) with adaptive thresholding is proposed to solve the problem of detection and estimation of surface defects on metal coating surfaces. The proposed method is developed through implementing changes to COA and improved the performance. For improving capability of local search as well to keep the global search effect, the enhanced methods such as level set is associated with the proposed method. Also, the method adapts dynamic step size, adaptively changing with the search process for improving the rate of convergence and the ability of local search. The algorithm performance is scrutinized from the experimental analysis and results. Also, the segmentation effectiveness is further enhanced by adapting suitable methods for preprocessing and post processing. The comparison and analysis of the results accomplished with the proposed method and results of earlier methods shows superior performance of the proposed method.
Highlights
The quality control is a significant feature of today‟s highly competitive industry
In this paper we propose Modified Adaptive Thresholding Method using Cuckoo Search
It is clear from the table and graph, the results obtained from the proposed method gives better result compared to other three techniques such as adaptive thresholding with Particle swarm optimization (PSO), median based Otsu and Otsu‟s method for defect detection and segmentation
Summary
The quality control is a significant feature of today‟s highly competitive industry. Each manufacturing process output inspection is an imperative way to enrich the end product quality. Automated visual inspection is an exceptionally imperative non-contact method in industries It detects diminutive defects which turn out as local anomalies relative to the adjacent background in the acquired image. A robust automated visual inspection method for identifying restrained defects in the pattern surface and the nondestructive testing technique become commonly used method for defect detection and classification. The commonly used automated surface inspection algorithms [5] construct local features for surface defect detection. The machine vision based method utilized for surface inspection as a non-contact detection technology in this decade which is capable to track the dynamic details of the object surface. In this paper we propose Modified Adaptive Thresholding Method using Cuckoo Search [9] Algorithm for Detecting Surface Defects, which is flexible to accord with these problems and further improves the segmentation performance. The proposed method is compared with existing thresholding methods
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