Deep learning aplicado à inspeção visual de madeira, pólen e vírus

Abstract

Pollen, virus, and wood species recognition has been shown to be an important task for a number of areas, contributing to criminal investigations, climate studies, drug developments, among others. However, these studies rely on highly qualified professionals to analyze microscopic images, which have become scarce and costly. Therefore, the automation of these tasks using computational methods is promising. Recently, Deep Learning has proven to be the ultimate set of techniques for many computer vision tasks, however, it is a very difficult task to build a data set with enough samples to train these techniques from scratch. In this study, the use of transfer learning was investigated pre-training deep neural networks for pollen, wood, and virus image classification and compared their results with training from scratch and with promising pre-designed features. It also introduced the largest data sets of pollen and wood images to the present date, with 134 and 281 classes, respectively. Results indicate that even with a high number of classes, the proposed methodologies are capable of achieving acceptable classification accuracy: 96.24% in the pollen and 98.75% in the wood classification. Additionally, a new portable device combined with a new image acquisition protocol was developed to perform the wood identification outside a laboratory. Initial results evaluating 11 amazon wood species achieved 98.13% accuracy. As for the virus classification, the deep neural networks have shown to be more effective achieving 89% accuracy, being 2.8% superior when compared to the best pre-designed features study in the literature.