Article Dans Une Revue IEEE Access Année : 2025

A Benchmark of Neural Networks for Semantic Segmentation of Wood Log Ends

Résumé

Neural networks have achieved significant performance in image segmentation tasks. This paper describes a benchmark of deep learning-based methods for segmenting log ends of several species in images. More precisely, we are interested in segmenting a cross-section of a log positioned approximately at the centre of the image. Other logs that may be present next to the log of interest in the image should not be segmented. This task of semantic segmentation is essential in various applications, such as biometric log tracking and log quality assessment. Different state-of-the-art Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for segmentation were tested in this paper, including Mask R-CNN, U-Net, PointRend, YOLOv11 and OneFormer. Some of these, being among the most recent architectures, had never before been tested on this type of image. To compare efficiency of the networks, well-known metrics such as Precision, Recall, Accuracy, Dice, IoU and MCC were used. From the experiment results, PointRend found to produce the most accurate segmentations, while YOLOv11 was better suited to real-time processing. In addition, an original dataset consisting of 1345 manually segmented images of oak log ends was created for this study and made available. Oak is a new species in these segmentation approaches. This dataset was supplemented with 855 images of two softwood species (Douglas fir and spruce) in order to increase the genericity of the models. The images have been selected to represent real situations encountered in the field and a wide variety of acquisition conditions and cross-section appearance.

Recherche Data Gouv

Cite 10.57745/RKDTKY Jeu de données Longuetaud, Fleur; Mothe, Frédéric; Martinetto, Dorian; Ngo, Phuc; Rittié, Daniel; Bordat, Frédéric; Jacquin, Philippe; Vuidel, Bruno; Piboule, Alexandre; Debled-Rennesson, Isabelle; Albert, Aymeric; Richter, Claudine, 2025, "Traceability of oak logs: Extension of the Biomtrace database for the study of extreme cases", https://doi.org/10.57745/RKDTKY, Recherche Data Gouv, V1

Dates et versions

hal-04644460 , version 1 (11-07-2024)

Identifiants

Citer

Dorian Martinetto, Georg Wimmer, Phuc Ngo, Frédéric Mothe, Alexandre Piboule, et al.. A Benchmark of Neural Networks for Semantic Segmentation of Wood Log Ends. IEEE Access, 2025, 13, pp.180344 -- 180354. ⟨10.1109/ACCESS.2025.3622419⟩. ⟨hal-04644460⟩
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