Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends

Résumé

In this paper, wood cross-section (CS) segmentationof RGB images is treated. CS segmentation has already been studied for computed tomography images, but few study focuses on RGB images. CS segmentation in rough log ends is an important feature for the both assessment of wood quality and wood traceability. Indeed, it allows to extract other features like pith, eccentricity (distance between the pith and the geometriccentre) or annual tree rings which are related to mechanical strength. In image processing, neural networks have been widely used to solve the problem of objects segmentation. In this paper, we propose to compare different state-of-the-art neural networks for CS segmentation task. In particular, we consider U-Net, Mask R-CNN, RefineNet and SegNet. We create an imageset which has been split into 6 subsets . Considered neural networks have been trained on each subset in order to compare their performance on different type of images. Results show different behaviors between neural networks. On the one hand, overall U-Net learns better on small dataset than the others. On the other hand, RefineNet learns well on huge dataset. While SegNetis less efficient and Mask R-CNN does not provide a detailed segmentation. This offers a preliminary result on neural network performances for CS segmentation.

Domaines

Informatique
Fichier principal
Vignette du fichier
IPAS_2020_camera_ready_Log_Ends_Segmentation-2.pdf (2.18 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03058259 , version 1 (11-12-2020)

Identifiants

  • HAL Id : hal-03058259 , version 1

Citer

Rémi Decelle, Ehsaneddin Jalilian. Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends. Fourth IEEE International Conference on Image Processing, Applications and Systems (IPAS 2020), Sep 2019, Gênes, Italy. ⟨hal-03058259⟩
133 Consultations
253 Téléchargements

Partager

Gmail Facebook X LinkedIn More