A Deep Learning-Based Approach for Automatic Leather Classification in Industry 4.0

Smart production is trying to bring companies into the world of industry 4.0. In this field, leather is a natural product commonly used as a raw material to manufacture luxury objects. To ensure good quality on these products, one of the fundamental processes is the visual inspection phase to identify defects on leather surfaces. A typical exercise in quality control during the production is to perform a rigorous manual inspection on the same piece of leather several times, using different viewing angles and distances. However, the process of the human inspection is expensive, time-consuming, and subjective. In addition, it is always prone to human error and inter-subject variability as it requires a high level of concentration and might lead to labor fatigue. Therefore, there is a necessity to develop an automatic vision-based solution in order to reduce manual intervention in this specific process. In this regard, this work presents an automatic approach to perform leather and stitching classification. The main goal is to automatically classify the images inside of a new dataset called LASCC (Leather And Stitching Color Classification) dataset. The dataset is newly collected and it is composed of 67 images with two different colors of leathers and seven different colors of stitching. For this purpose, Deep Convolutional Neural Networks (DCNNs) such as VGG16, Resnet50 and InceptionV3 have been applied to LASCC dataset, on a sample of 67 images. Experimental results confirmed the effectiveness and the suitability of the approach, showing high values of accuracy