mlsanityData to 14 Oct 2026

WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images

arXiv 2411.11738 ↗First posted 18 Nov 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 18 Nov 2024.

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34 of 51 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2020End-to-End Object Detection with Transformerscited by 7,414
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  7. 2017Squeeze-and-Excitation Networkscited by 4,789
  8. 2022A ConvNet for the 2020scited by 3,579
  9. 2018YOLOv3: An Incremental Improvementcited by 2,659
  10. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  11. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  12. 2016YOLO9000: Better, Faster, Strongercited by 1,431
22 more
  1. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  2. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
  3. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  4. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  5. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
  6. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
  7. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  8. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  9. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  10. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  11. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  12. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  13. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  14. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178
  15. 2020Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentationcited by 98
  16. 2022PP-YOLOE: An evolved version of YOLOcited by 75
  17. 2020PP-YOLO: An Effective and Efficient Implementation of Object Detectorcited by 44
  18. 2022DAMO-YOLO : A Report on Real-Time Object Detection Designcited by 38
  19. 2022NMS Strikes Backcited by 33
  20. 2021PP-YOLOv2: A Practical Object Detectorcited by 30
  21. 2023Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learningcited by 3
  22. 2024Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learningcited by 1

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