Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
Preprint
Abstract
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.Keywords
All Related Versions
This publication has 15 references indexed in Scilit:
- YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors2023
- Accelerating DETR Convergence via Semantic-Aligned Matching2022
- Emerging Properties in Self-Supervised Vision Transformers2021
- Test-time augmentation for deep learning-based cell segmentation on microscopy imagesScientific Reports, 2020
- CenterNet: Keypoint Triplets for Object Detection2019
- CornerNet: Detecting Objects as Paired KeypointsInternational Journal of Computer Vision, 2019
- Deep Residual Learning for Image Recognition2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- Optimal Thresholding of Classifiers to Maximize F1 Measure2014
- The Pascal Visual Object Classes (VOC) ChallengeInternational Journal of Computer Vision, 2009