Defective texture classification using optimized neural network structure.

• Feature extraction alone does not increase the accuracy of defect detection. • The usage of Neural Networks increases the accuracy with the feature extraction method. • Nine different training algorithms tested and Levenberg–Marquardt is selected based on its high accuracy. • Use of Optimized Neural Networks further enhanced the accuracy in the detection of almost all types of defects. • Developed Optimized Neural Network classifier using GA, PSO, ABC. ABC-ANN outperforms the other two. It is essential to identify the defects on leather sheets in leather industries as part of quality control. Manual inspection is often considered inconsistent due to a lack of accuracy and time constraints. Automation of the process using a machine vision system gives high accuracy and consistency in the classification of the leather sheets basing on their quality. In this paper, a technique to detect defects on wet-blue leather and classifying them using artificial neural networks (ANN) is proposed. Features of several defects on the leather are extracted using a GreyLevelCo-occurrence matrix(GLCM) and GreyLevelRunLengthMatrix(GLRLM). The obtained features are given to a multi-layer perceptron with a Levenberg–Marquardt (LM) algorithm. The numeral of HiddenLayers (N HL) and the numeral of neurons in each HiddenLayer(NN HL) of the network are optimized using a genetic algorithm(GA), particle swarm optimization (PSO) and artificial bee colony optimization(ABC). The simulation-based experimentation is carried out in MATLAB. Classifier outputs are analyzed using performance metrics like specificity, accuracy, and sensitivity. The LM-ANN based model with ABC optimization outperforms the other two techniques in classifying the defects of wet blue leather with performance metrics with 98.73% of Specificity, 97.85% of Accuracy and 94.14% of Sensitivity. [ABSTRACT FROM AUTHOR]