Deep Learning methodology for the identification of wood species using high-resolution macroscopic images
Metadatos
Mostrar el registro completo del ítemAutor
Herrera-Poyatos, David; Herrera-Poyatos, Andrés; Montes, Rosana; Palacios, Paloma de; Esteban, Luis G.; García Iruela, Alberto; García Fernández, Francisco; Herrera Triguero, FranciscoEditorial
IEEE
Materia
Wood species identification Deep learning Convolutional neural networks Patch-based inference voting classification
Fecha
2024-06-17Referencia bibliográfica
Published version: D. Herrera-Poyatos et al., "Deep Learning methodology for the identification of wood species using high-resolution macroscopic images," 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-8, doi: 10.1109/IJCNN60899.2024.10650590
Patrocinador
Ministry of Science and Technology of Spain PID2020-119478GB-I00; Ministerio de Agricultura, Pesca y Alimentación; Fondo Europeo Agrícola de Desarrollo Rural (FEADER)Resumen
Significant advancements in the field of wood species identification are needed worldwide to support sustainable timber trade. In this work we contribute to automate the identification of wood
species via high-resolution macroscopic images of timber. The main challenge of this problem is
that fine-grained patterns in timber are crucial in order to accurately identify wood species, and
these patterns are not properly learned by traditional convolutional neural networks (CNNs) trained
on low/medium resolution images.
We propose a Timber Deep Learning Identification with Patch-based Inference Voting methodology,
abbreviated TDLI-PIV methodology. Our proposal exploits the concept of patching and the availability of high-resolution macroscopic images of timber in order to overcome the inherent challenges
that CNNs face in timber identification. The TDLI-PIV methodology is able to capture fine-grained
patterns in timber and, moreover, boosts robustness and prediction accuracy via a collaborative voting inference process.
In this work we also introduce a new data set of marcroscopic images of timber, called GOIMAI-Phase-I, which has been obtained using optical magnification in order to capture fine-grained details, which contrasts to the other datasets that are publicly available. More concretely, images in
GOIMAI-Phase-I are taken with a smartphone with a 24x magnifying lens attached to the camera.
Our data set contains 2120 images of timber and covers 37 legally protected wood species.
Our experiments have assessed the performance of the TDLI-PIV methodology, involving the comparison with other methodologies available in the literature, exploration of data augmentation methods and the effect that the dataset size has on the accuracy of TDLI-PIV.