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Deep learning for the detection of semantic features in tree X-ray CT scans

Artificial Intelligence in Agriculture 2023年第1期7卷 13-26页

作者： Salim Khazem Antoine Richard Jeremy Fix Cédric Pradalier georgiatech-CNRS IRL 2958 MetzFrance CentraleSupélec MetzFrance georgiatech lorraine MetzFrance LORIA CNRS UMR 7503MetzFrance University of Luxembourg Campus Kirchberg6rue Richard Coudenhove-Kalergi L-1359 Luxembourg

According to the industry,the value of wood logs is heavily influenced by their internal structure,particularly the distribution of knots within the ***,CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of *** where the tree semantic features,especially knots,contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log ***,this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline,knot localization and log contour,in a robust and efficient *** this paper,we propose an effective methodology based on deep learning for performing these different tasks by processing CTscanner images with deep convolutional neural *** meet this objective,three end-to-end trainable pipelines are *** first pipeline is focused on centers detection using CNNs architecture with a regression head,the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder *** different architectures are tested on several tree *** these experiments,we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of *** main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

关键词： X-rays images Deep learning Convolutional neural networks Image segmentation Wood knots Coordinates detection Contour estimation

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