Article(id=1322924670567195495, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.07.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750867200000, receivedDateStr=2025-06-26, revisedDate=1762963200000, revisedDateStr=2025-11-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339420215, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339420215, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339420215, creator=13701087609, updateTime=1793339420215, updator=13701087609, issue=Issue{id=1322924574345670676, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='7', pageStart='1', pageEnd='234', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339397274, creator='13701087609', updateTime=1793344662547, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946659029746602, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946659029746603, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=182, endPage=192, ext={EN=ArticleExt(id=1322924671938732904, articleId=1322924670567195495, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Improved RT-DETR model for real-time detection of particleboard defects, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】

This study proposed a high-precision, high-efficiency method for surface defect detection of particleboard that is suitable for practical industrial scenarios to address the problems of high missed detection rate, slow detection speed, and insufficient ability to identify complex defects associated with traditional detection methods in current particleboard production, thereby achieving a dual optimization of detection accuracy and real-time performance.

【Method】

Based on RT-DETR-r18, ablation experiments were conducted using the control variable method to verify the effectiveness of the three key modules: ECG-CFNet, FSA, and MPCA. Several mainstream lightweight backbone networks, including Swin Transformer and VanillaNet, were substituted for comparison of their training performance. The improved model was further compared with SSD, Faster-RCNN, YOLO series, and the original RT-DETR to validate its superiority. Key indicators such as mAP@50 and GFLOPs were adopted, combined with performance curves, PR curves, and visualization results, to comprehensively evaluate the detection accuracy, efficiency, and industrial applicability of the model.

【Result】

Comparison experimental results on standard defect image datasets show that the proposed improved algorithm achieves 91.9% in terms of mean detection accuracy (mAP@50), which is 2.9% higher than the original RT-DETR-r18 model. At the same time, the number of parameters of the model and the computational complexity are both decreased, which suggests that while guaranteeing the improvement of the detection accuracy, the detection efficiency is optimized as well. Specifically, in the detection of typical defect types such as cracks, shavings and black spots, both show higher recognition rates and lower false alarm rates.

【Conclusion】

The improved RT-DETR algorithm significantly improves the accuracy and real-time performance of particleboard surface defect detection while maintaining low computational resource consumption, and has strong industrial deployability and application prospects.

, authors=Tao Zhang, Hanlong Xiao, Chunmei Yang, Jie Yan, Liangquan Zhou, Tongle Jia, authorsList=Tao Zhang, Hanlong Xiao, Chunmei Yang, Jie Yan, Liangquan Zhou, Tongle Jia, authorCompany=null, correspAuthors=Chunmei Yang, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Central South University of Forestry & Technology, copyrightOwner=Journal of Central South University of Forestry & Technology, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322924672341386089, articleId=1322924670567195495, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=改进RT-DETR模型的刨花板缺陷实时检测方法, columnId=1322924330723713471, journalTitle=中南林业科技大学学报, columnName=林业工程, runingTitle=null, highlight=null, articleAbstract=
【目的】

提出一种高精度、高效率且适用于工业实际场景的刨花板表面缺陷检测方法,以解决当前刨花板生产中传统检测方法漏检率高、检测速度慢及复杂缺陷识别能力不足的问题,实现缺陷检测准确性与实时性的双重优化。

【方法】

以RT-DETR-r18为基准,用控制变量法开展消融试验,验证ECG-CFNet、FSA、MPCA 3大模块有效性;替换SwinTransformer和VanillaNet等主流轻量化主干网络,对比其训练效果;与SSD、Faster-RCNN、YOLO系列及原始RT-DETR对比,验证改进模型优越性;选取mAP@50、GFLOPs等关键指标,结合性能曲线、PR曲线及可视化结果,全面评价模型检测精度、效率及工业适配性。

【结果】

在标准缺陷图像数据集上的对比试验结果表明,所提出的改进算法在平均检测精度(mAP@50)方面达到91.9%,较原始RT-DETR-r18模型提升了2.9%;同时,模型的参数量和计算复杂度均有所下降,表明在保障检测精度提升的同时,检测效率也得到优化。在裂纹、刨花和黑斑等典型缺陷类型的检测中,改进模型均表现出更高的识别率和更低的误报率。

【结论】

改进后的RT-DETR算法在保持较低计算资源消耗的前提下,显著提升了刨花板表面缺陷检测的精度和实时性,具备较强的工业可部署性和应用前景。

, authors=张韬, 肖汉龙, 杨春梅, 闫杰, 周良全, 贾曈乐, authorsList=张韬, 肖汉龙, 杨春梅, 闫杰, 周良全, 贾曈乐, authorCompany=null, correspAuthors=杨春梅, authorNote=

张韬(),副教授,博士研究生导师。

, correspAuthorsNote=
杨春梅(),教授,博士,博士研究生导师。
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改进RT-DETR模型的刨花板缺陷实时检测方法
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张韬 , 肖汉龙 , 杨春梅 , 闫杰 , 周良全 , 贾曈乐
中南林业科技大学学报 | 林业工程 2026,46(7): 182-192
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中南林业科技大学学报 |林业工程 2026 , 46 (7) : 182 -192
改进RT-DETR模型的刨花板缺陷实时检测方法
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张韬 , 肖汉龙, 杨春梅 , 闫杰, 周良全, 贾曈乐
作者信息
  • 东北林业大学 机电工程学院,黑龙江 哈尔滨 150040
通讯作者:
杨春梅(),教授,博士,博士研究生导师。
作者简介:

张韬(),副教授,博士研究生导师。

Improved RT-DETR model for real-time detection of particleboard defects
Tao Zhang , Hanlong Xiao, Chunmei Yang , Jie Yan, Liangquan Zhou, Tongle Jia
Affiliations
  • College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China
出版时间: 2026-07-25 doi: 10.14067/j.cnki.1673-923x.2026.07.017
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【目的】

提出一种高精度、高效率且适用于工业实际场景的刨花板表面缺陷检测方法,以解决当前刨花板生产中传统检测方法漏检率高、检测速度慢及复杂缺陷识别能力不足的问题,实现缺陷检测准确性与实时性的双重优化。

【方法】

以RT-DETR-r18为基准,用控制变量法开展消融试验,验证ECG-CFNet、FSA、MPCA 3大模块有效性;替换SwinTransformer和VanillaNet等主流轻量化主干网络,对比其训练效果;与SSD、Faster-RCNN、YOLO系列及原始RT-DETR对比,验证改进模型优越性;选取mAP@50、GFLOPs等关键指标,结合性能曲线、PR曲线及可视化结果,全面评价模型检测精度、效率及工业适配性。

【结果】

在标准缺陷图像数据集上的对比试验结果表明,所提出的改进算法在平均检测精度(mAP@50)方面达到91.9%,较原始RT-DETR-r18模型提升了2.9%;同时,模型的参数量和计算复杂度均有所下降,表明在保障检测精度提升的同时,检测效率也得到优化。在裂纹、刨花和黑斑等典型缺陷类型的检测中,改进模型均表现出更高的识别率和更低的误报率。

【结论】

改进后的RT-DETR算法在保持较低计算资源消耗的前提下,显著提升了刨花板表面缺陷检测的精度和实时性,具备较强的工业可部署性和应用前景。

RT-DETR  /  缺陷检测  /  刨花板  /  实时检测
【Objective】

This study proposed a high-precision, high-efficiency method for surface defect detection of particleboard that is suitable for practical industrial scenarios to address the problems of high missed detection rate, slow detection speed, and insufficient ability to identify complex defects associated with traditional detection methods in current particleboard production, thereby achieving a dual optimization of detection accuracy and real-time performance.

【Method】

Based on RT-DETR-r18, ablation experiments were conducted using the control variable method to verify the effectiveness of the three key modules: ECG-CFNet, FSA, and MPCA. Several mainstream lightweight backbone networks, including Swin Transformer and VanillaNet, were substituted for comparison of their training performance. The improved model was further compared with SSD, Faster-RCNN, YOLO series, and the original RT-DETR to validate its superiority. Key indicators such as mAP@50 and GFLOPs were adopted, combined with performance curves, PR curves, and visualization results, to comprehensively evaluate the detection accuracy, efficiency, and industrial applicability of the model.

【Result】

Comparison experimental results on standard defect image datasets show that the proposed improved algorithm achieves 91.9% in terms of mean detection accuracy (mAP@50), which is 2.9% higher than the original RT-DETR-r18 model. At the same time, the number of parameters of the model and the computational complexity are both decreased, which suggests that while guaranteeing the improvement of the detection accuracy, the detection efficiency is optimized as well. Specifically, in the detection of typical defect types such as cracks, shavings and black spots, both show higher recognition rates and lower false alarm rates.

【Conclusion】

The improved RT-DETR algorithm significantly improves the accuracy and real-time performance of particleboard surface defect detection while maintaining low computational resource consumption, and has strong industrial deployability and application prospects.

RT-DETR  /  defect detection  /  particleboard  /  real-time detection
张韬, 肖汉龙, 杨春梅, 闫杰, 周良全, 贾曈乐. 改进RT-DETR模型的刨花板缺陷实时检测方法. 中南林业科技大学学报, 2026 , 46 (7) : 182 -192 . DOI: 10.14067/j.cnki.1673-923x.2026.07.017
Tao Zhang, Hanlong Xiao, Chunmei Yang, Jie Yan, Liangquan Zhou, Tongle Jia. Improved RT-DETR model for real-time detection of particleboard defects[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (7) : 182 -192 . DOI: 10.14067/j.cnki.1673-923x.2026.07.017
  • 国家重点研发计划项目(2024YFD2200700)
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2026年第46卷第7期
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doi: 10.14067/j.cnki.1673-923x.2026.07.017
  • 接收时间:2025-06-26
  • 首发时间:2026-10-30
  • 出版时间:2026-07-25
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  • 收稿日期:2025-06-26
  • 修回日期:2025-11-13
基金
国家重点研发计划项目(2024YFD2200700)
作者信息
    东北林业大学 机电工程学院,黑龙江 哈尔滨 150040

通讯作者:

杨春梅(),教授,博士,博士研究生导师。
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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