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.
In Image processing and computer vision, the optimization technique aims at producing improved end result from a set of possible inputs. It is considered to be an exceptional method for the detection of defects from coated surfaces. In the presence of on-site inspections, it is practically impossible to detect surface defects such as small cracks on the titanium-coated surfaces of components. In this type of overlooking surface cracks, a new procedure must be discovered and it can effectively assist and improve the detection rates. We recommend titanium-coated surface crack detection system. The proposed method is depends upon the spatially clustered pixels with same grey levels. For image segmentation an Optimized Adaptive Thresholding (OAT) approach is proposed, in which Particle Swarm Optimization (PSO) is used along with adaptive thresholding. The LDP(Local Directional Pattern) approach is utilized for measuring the image edges gradient values in distinct ways and determine the pixel values in a various directions. LDP algorithm is usually applied to spot the image edge. If no edges are detected, then the nearby pixels are checked and joint to a region class using region growing algorithm. Finally the performance of the proposed method can be evaluated in terms of parameter calculations to achieve the high accuracy, specificity and sensitivity of the crack image.
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