An Effective Surface Defect Detection Method Using Adaptive Thresholding Fused with PSO Algorithm

et al. 2019

IJACSA

Self Cite

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.

Section: Adaptive Thresholding With Psomentioning

confidence: 99%

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Aslam¹,

Santhi²,

et al. 2019

IJACSA

Self Cite

“…Optimization problems are very necessary in numerous branches of sciences and its main task is to discover the proper solution for an explicit problem. Consequently, the requisite in resolving optimization problems prompted the development of [8], artificial bee algorithm [9], gravitational search [10], Cuckoo search (CS) [11] ant colony algorithm [12], differential evolution [13], and Firefly (FF) algorithm [14] are the various optimization methods used. This paper proposes the adaptive thresholding with Firefly technique and level set method for identification and extraction of defects on the coated surface.…”

Section: Introductionmentioning

confidence: 99%

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

Aslam¹,

Santhi²,

et al. 2019

IJITEE

Self Cite

An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

“…The researchers make efforts in improvising optimization methods to prevail over problems in FCM clustering approach besides improve clusters characteristics. The combination of the adaptive thresholding with cuckoo search (ATCS) [10] and adaptive thresholding with particle swarm optimization methods (ATPSO) [11] are found to be efficient methods for defect detection techniques, but the lighting effects or shadows formed in images were found to decrease detection accuracy. The procedure of optimization is determining the appropriate solutions to the problems defined.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Coating Defects using Fuzzy C-Means Clustering with Firefly Optimization

Aslam*¹,

Santhi²,

et al. 2019

IJEAT

Self Cite

Defect detection in metallic surface images is a challenging task in the image analysis process. The data clustering and optimization techniques have been widely used for image segmentation and the combination of these two approaches improves the output stability as well as convergence speed. In this work developed an automatic, efficient method for the detection and segmentation of coating defects in metal surfaces. The Fuzzy c-means (FCM) and Firefly algorithm (FA) are well-known and popular methods to discover the image information comprising indiscriminate objects and solves many complex problems involved in image segmentation. In this paper, proposed a new technique for the coated metal surface defect detection using the hybridization of two methods, FCM with FA (FCM-FA). The results from experiments verified the efficiency of the developed FCM with FA over comparison with three existing methods in terms of evaluation parameters of defect detection for scanned high resolution images. It can be seen from the experimental results that the incorporated algorithm has the potential to segment and identify the defected regions from the coated surface.