Classification of similar electronic components by transfer learning methods

Proper selection of electronic components and automated component identification is critical for fast production processes in industry. In addition, for Internet of Things (IoT) systems, accurate and fast selection of similar electronic components is an important problem. In this study, a transfer learning-based method is proposed to classify electronic components that are difficult to select due to their similarity. Eight different convolutional neural network (CNN) models and a novel model developed only in this study were used to classify electronic components. In addition to the transfer learning methods, the parallel CNN method, in which hyperparameter determination is done by trial and error, was developed and used to solve the classification problem. In addition to the transfer learning method, the parameters were tried to be determined by the trial-and-error method for hyperparameter selection. The effect of batch size and learning rate hyperparameter variations on the prediction success of parallel CNN models is analyzed. The effect of two different batch sizes and learning rate values for transfer learning models is also analyzed. Metrics such as confusion matrix, accuracy, and loss were used for evaluation methods. The number of parameters and runtime metrics of the models were also evaluated. All experiments were averaged to obtain a general intuition of success. The success of the proposed method is given by the evaluation metrics. According to the accuracy metric, the Densely Connected Convolutional Networks (DenseNet-121) model was the most successful model with a value of 98.2925%.