林业科学2025，Vol.61Issue(11)：150-159,10.DOI:10.11707/j.1001-7488.LYKX20250012
基于深度学习的结构用锯材节子在线检测方法
Online Detecting Method of Structural Lumber Knot Based on Deep Learning
摘要
Abstract
[Objective]To address the low efficiency and strong subjectivity of manual visual grading for structural sawn timber,this study selected Pinus densiflora structural sawn timber and developed a deep-learning-based online knot detection method,providing technical support for improving the automation and accuracy of structural sawn timber grading.[Method]Based on the YOLO network,a knot-detection model was built that incorporates the efficient layer aggregation network(ELAN)and an image stitching,segmentation,and fusion scheme guided by SIFT features,strengthening the adaptability of the machine-vision defect-detection system to sawn-timber grading and other complex on-line vision tasks.Multi-scale prediction and loss-function minimization suppress background clutter and noise,enabling accurate classification and localization losses and thus raising knot-detection accuracy while optimizing task-specific performance.[Result]In the industrial production application site,the test results showed that the identification and defect detection accuracy of knots on the surface of lumber is 90.97%,with a missed detection rate of 9.03%.The average detection accuracy of knot defect location X and Y was 86.29%,the average detection accuracy of knot defect size L and W was 85.95%,and the detection speed could reach 20-30 m·min-1,which meets the practical application of wooden product processing line.[Conclusion]In this study,the deep learning method is suitable for lumber detection in practical application,reducing the subjectivity of manual inspection and improving the accuracy and efficiency of inspection.Machine vision detection technique promotes the innovation and development of wood grading technology,improves the quality of wood processing industry,and improves the technical level of wood structure construction industry.关键词
赤松结构用锯材/机器视觉节子检测平台/深度学习/YOLO 训练模型/高效层聚合网络Key words
Pinus densiflora structural lumber/machine vision knot inspection platform/deep learning/YOLO training model/efficient layer aggregation network分类
农业科技引用本文复制引用
纪敏,高锐,王晓欢,刁兴良,韩佳锴,赵扬,王国富,张伟..基于深度学习的结构用锯材节子在线检测方法[J].林业科学,2025,61(11):150-159,10.基金项目
国家重点研发计划项目(2024YFD2200700) （2024YFD2200700）
福建省科技重大专项(2024HZ026011). （2024HZ026011）
