Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm
Open accessAbstract
Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%.
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A SSD detector with DenseNet backbone for wood defects obtained up to 96.1% mean average precision.
“Even though they used it for a particular case of detecting defects in the Akagi and Pinus sylvestris trees, they obtained up to 96.1% mean average precision, which is great.”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [42]
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References 31 · Cited by 111
Research works printed but not linked: 3
- [1]Fan J. Liu Y. Hu Z. Zhao Q. Shen L. Zhou X. Solid wood panel defect detection and recognition system based on Faster R-CNN J. For. Eng. 2019 4 112 117 10.13360/j.issn.2096-1359.2019.03.017 · DOI
- [25]Girshick R. Fast R-CNN Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) Santiago, Chile 11–18 December 2015 1440 1448
- [32]Xiao J.Y. Ma Y. Zhou Z. Fang Y.M. Detection of Timber Surface Knots Based on Faster R-CNN China Wood Ind. 2020 34 45 48 10.19455/j.mcgy.20200211 · DOI
- Authors
- Ding, F., Zhuang, Z., Liu, Y., Jiang, D., Yan, X., & Wang, Z.
- Affiliations
- Nanjing Forestry University (CN)
- Corresponding author
- Liu, Ying
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-09-17
- Volume
- 20
- Issue
- 18
- Pages
- 5315
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7570981
- OpenAlex
- W3086841682
- Cited by
- 111
- References
- 31
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 25 · 2023: 24 · 2022: 18 · 2021: 13
- FWCI
- 8.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the 2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology (BE2019112)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- wood defect detection, solid wood panels, Single Shot MultiBox Detector, DenseNet, transfer learning, machine vision, wood knots, Pinus sylvestris, defect classification
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