Wood Panel Defect Detection Based on Improved YOLOv8n
Wood panel surface defect detection is critical to product quality. Traditional detection methods are time-consuming and subjective, and they can lead to economic waste, while deep learning image recognition techniques offer a new approach. However, the accuracy and convergence speed of existing def...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
North Carolina State University
2025-02-01
|
| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24141 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736504409063424 |
|---|---|
| author | Rui Li Shilu Zhong Xuemei Yang |
| author_facet | Rui Li Shilu Zhong Xuemei Yang |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Wood panel surface defect detection is critical to product quality. Traditional detection methods are time-consuming and subjective, and they can lead to economic waste, while deep learning image recognition techniques offer a new approach. However, the accuracy and convergence speed of existing defect detection techniques still require improvement. In this paper, an improved algorithm based on YOLOv8n was designed for accurate detection of wood panel defects. The C-ADown method was designed to replace traditional downsampling, while preserving high-frequency features. The combination of the Dilation-wise Residual Module and multi-scale dilation attention was employed to enhance the multiscale robustness of defect detection. A hybrid encoder was added to improve localization accuracy. The loss function was optimized to improve detection accuracy and convergence speed. Compared to the base YOLOv8 version, the improved model achieved a 6.1% increase in mAP, an 8% increase in recall, and a 3.6% increase in precision, significantly enhancing the model’s detection capabilities. The GitHub link to the improved algorithm files is as follows: (https://github.com/humblefactos1/YOLOV8-CDC/tree/main.) |
| format | Article |
| id | doaj-art-ec9553d796c043e8935e4419d1824f35 |
| institution | DOAJ |
| issn | 1930-2126 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj-art-ec9553d796c043e8935e4419d1824f352025-08-20T03:07:16ZengNorth Carolina State UniversityBioResources1930-21262025-02-01202255625732384Wood Panel Defect Detection Based on Improved YOLOv8nRui Li0Shilu Zhong1Xuemei Yang2College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaWood panel surface defect detection is critical to product quality. Traditional detection methods are time-consuming and subjective, and they can lead to economic waste, while deep learning image recognition techniques offer a new approach. However, the accuracy and convergence speed of existing defect detection techniques still require improvement. In this paper, an improved algorithm based on YOLOv8n was designed for accurate detection of wood panel defects. The C-ADown method was designed to replace traditional downsampling, while preserving high-frequency features. The combination of the Dilation-wise Residual Module and multi-scale dilation attention was employed to enhance the multiscale robustness of defect detection. A hybrid encoder was added to improve localization accuracy. The loss function was optimized to improve detection accuracy and convergence speed. Compared to the base YOLOv8 version, the improved model achieved a 6.1% increase in mAP, an 8% increase in recall, and a 3.6% increase in precision, significantly enhancing the model’s detection capabilities. The GitHub link to the improved algorithm files is as follows: (https://github.com/humblefactos1/YOLOV8-CDC/tree/main.)https://ojs.bioresources.com/index.php/BRJ/article/view/24141wood panel deep learningyolov8nc-adowndilation-wise residualmulti-scale dilation attentionloss function |
| spellingShingle | Rui Li Shilu Zhong Xuemei Yang Wood Panel Defect Detection Based on Improved YOLOv8n BioResources wood panel deep learning yolov8n c-adown dilation-wise residual multi-scale dilation attention loss function |
| title | Wood Panel Defect Detection Based on Improved YOLOv8n |
| title_full | Wood Panel Defect Detection Based on Improved YOLOv8n |
| title_fullStr | Wood Panel Defect Detection Based on Improved YOLOv8n |
| title_full_unstemmed | Wood Panel Defect Detection Based on Improved YOLOv8n |
| title_short | Wood Panel Defect Detection Based on Improved YOLOv8n |
| title_sort | wood panel defect detection based on improved yolov8n |
| topic | wood panel deep learning yolov8n c-adown dilation-wise residual multi-scale dilation attention loss function |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24141 |
| work_keys_str_mv | AT ruili woodpaneldefectdetectionbasedonimprovedyolov8n AT shiluzhong woodpaneldefectdetectionbasedonimprovedyolov8n AT xuemeiyang woodpaneldefectdetectionbasedonimprovedyolov8n |