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.

识别皮革行业皮革片材上的缺陷是至关重要的，这是质量控制的一部分。由于缺乏准确性和时间限制，通常认为手动检查是不一致的。使用机器视觉系统对过程进行自动化，可以根据皮革的质量在皮革分类中提供高精度和一致性。本文提出了一种检测湿蓝色皮革中缺陷的技术，并使用人工神经网络对其进行分类。使用GreyLevel共生矩阵（GLCM）和GreyLevelRunLengthMatrix（GLRLM）提取皮革上几个缺陷的特征。使用Levenberg-Marquardt（LM）算法将获得的特征赋予多层感知器。HiddenLayers（N _HL的数字使用遗传算法（GA），粒子群优化（PSO）和人工蜂群优化（ABC）对网络的每个HiddenLayer（NN _HL）中的神经元数量进行优化。基于仿真的实验是在MATLAB中进行的。使用性能指标（如特异性，准确性和敏感性）分析分类器的输出。具有ABC优化功能的基于LM-ANN的模型在对湿蓝色皮革的缺陷进行分类方面的性能优于其他两种技术，其性能指标为98.73％的特异性，97.85％的准确性和94.14％的灵敏度。