Wood Polish Classification for Automated Quality Inspection based on AI Vision

Nowadays, the demand for quality inspection of wood polishing is increasing. Thus, there is a need on industrial level to maintain high quality inspection. The quality inspection on wood polishing is currently done by human labors, which is inefficient, costly, and time-consuming. To reduce the cost of wood quality inspection, we propose an automated quality inspection based on AI vision to distinguish whether the wood is polished or unpolished. This system uses a deep learning method to classify polished or unpolished wood, which is one of the pioneer works using deep learning to examine wood quality. In this paper, we adopt the Efficient Net architecture because of its superior capability of handling the model parameters. The proposed approach combines Adam optimizer and SoftMax classifiers to provide the better performance of the model. This paper presents the binary classification on our dataset that contains 1,920 training and 560 test images. The result showed an average accuracy of 85%. In addition, the Efficient Net indicated the competitive performance metric of 85 % as recall, 85.5 % as precision, and 85 % as f1-score. In conclusion, the proposed architecture is satisfactory for automated quality inspection in the wood polishing process.