WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability
Structural timber is an important building material, but surface defects such as cracks and knots seriously affect its load-bearing capacity, dimensional stability, and long-term durability, posing a significant risk to structural safety. Conventional inspection methods are unable to address the iss...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/13/2281 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849319659621318656 |
|---|---|
| author | Xiaoxia Lin Weihao Gong Lin Sun Xiaodong Yang Chunwei Leng Yan Li Zhenyu Niu Yingzhou Meng Xinyue Xiao Junyan Zhang |
| author_facet | Xiaoxia Lin Weihao Gong Lin Sun Xiaodong Yang Chunwei Leng Yan Li Zhenyu Niu Yingzhou Meng Xinyue Xiao Junyan Zhang |
| author_sort | Xiaoxia Lin |
| collection | DOAJ |
| description | Structural timber is an important building material, but surface defects such as cracks and knots seriously affect its load-bearing capacity, dimensional stability, and long-term durability, posing a significant risk to structural safety. Conventional inspection methods are unable to address the issues of multi-scale defect characterization, inter-class confusion, and morphological diversity, thus limiting reliable construction quality assurance. To overcome these challenges, this study proposes WDNET-YOLO: an enhanced deep learning model based on YOLOv8n for high-precision defect detection in structural wood. First, the RepVGG reparameterized backbone utilizes multi-branch training to capture critical defect features (e.g., distributed cracks and dense clusters of knots) across scales. Second, the ECA attention mechanism dynamically suppresses complex wood grain interference and enhances the discriminative feature representation between high-risk defect classes (e.g., cracks vs. knots). Finally, CARAFE up-sampling with adaptive contextual reorganization improves the sensitivity to morphologically variable defects (e.g., fine cracks and resin irregularities). The analysis results show that the mAP50 and mAP50-95 of WDNET-YOLO are improved by 3.7% and 3.5%, respectively, compared to YOLOv8n, while the parameters are increased by only 4.4%. The model provides a powerful solution for automated structural timber inspection, which directly improves building safety and reliability by preventing failures caused by defects, optimizing material utilization, and supporting compliance with building quality standards. |
| format | Article |
| id | doaj-art-fa6ae746f99c4fedbb6a2a5d1fe79828 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-fa6ae746f99c4fedbb6a2a5d1fe798282025-08-20T03:50:21ZengMDPI AGBuildings2075-53092025-06-011513228110.3390/buildings15132281WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and ReliabilityXiaoxia Lin0Weihao Gong1Lin Sun2Xiaodong Yang3Chunwei Leng4Yan Li5Zhenyu Niu6Yingzhou Meng7Xinyue Xiao8Junyan Zhang9College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaHanqing Data Consulting Co., Ltd., Zibo 255000, ChinaHanqing Data Consulting Co., Ltd., Zibo 255000, ChinaHanqing Data Consulting Co., Ltd., Zibo 255000, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Taian 271001, ChinaStructural timber is an important building material, but surface defects such as cracks and knots seriously affect its load-bearing capacity, dimensional stability, and long-term durability, posing a significant risk to structural safety. Conventional inspection methods are unable to address the issues of multi-scale defect characterization, inter-class confusion, and morphological diversity, thus limiting reliable construction quality assurance. To overcome these challenges, this study proposes WDNET-YOLO: an enhanced deep learning model based on YOLOv8n for high-precision defect detection in structural wood. First, the RepVGG reparameterized backbone utilizes multi-branch training to capture critical defect features (e.g., distributed cracks and dense clusters of knots) across scales. Second, the ECA attention mechanism dynamically suppresses complex wood grain interference and enhances the discriminative feature representation between high-risk defect classes (e.g., cracks vs. knots). Finally, CARAFE up-sampling with adaptive contextual reorganization improves the sensitivity to morphologically variable defects (e.g., fine cracks and resin irregularities). The analysis results show that the mAP50 and mAP50-95 of WDNET-YOLO are improved by 3.7% and 3.5%, respectively, compared to YOLOv8n, while the parameters are increased by only 4.4%. The model provides a powerful solution for automated structural timber inspection, which directly improves building safety and reliability by preventing failures caused by defects, optimizing material utilization, and supporting compliance with building quality standards.https://www.mdpi.com/2075-5309/15/13/2281structural timber defect detectiontimber quality controlYOLOv8building materials |
| spellingShingle | Xiaoxia Lin Weihao Gong Lin Sun Xiaodong Yang Chunwei Leng Yan Li Zhenyu Niu Yingzhou Meng Xinyue Xiao Junyan Zhang WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability Buildings structural timber defect detection timber quality control YOLOv8 building materials |
| title | WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability |
| title_full | WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability |
| title_fullStr | WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability |
| title_full_unstemmed | WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability |
| title_short | WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability |
| title_sort | wdnet yolo enhanced deep learning for structural timber defect detection to improve building safety and reliability |
| topic | structural timber defect detection timber quality control YOLOv8 building materials |
| url | https://www.mdpi.com/2075-5309/15/13/2281 |
| work_keys_str_mv | AT xiaoxialin wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT weihaogong wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT linsun wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT xiaodongyang wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT chunweileng wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT yanli wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT zhenyuniu wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT yingzhoumeng wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT xinyuexiao wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability AT junyanzhang wdnetyoloenhanceddeeplearningforstructuraltimberdefectdetectiontoimprovebuildingsafetyandreliability |