文献类型：期刊文献

中文题名：面向刨花板表面多尺度缺陷正偏态分布的目标检测方法

英文题名：Object Detection Method for Positively Skewed Distribution of Multi-Scale Defects on Particleboard Surface

作者：刘恒[1] 郭浩盟[1] 戴蕙泽[1] 柴哲明[2,3] 李春育[4] 杨建华[1]

第一作者：刘恒

机构：[1]中国林业科学研究院木材工业研究所,北京100091;[2]哈尔滨工业大学仪器科学与工程学院,哈尔滨150006;[3]中国科学院深圳先进技术研究院,深圳518055;[4]唐县汇银木业有限公司,保定072350

年份：2025

卷号：61

期号：12

起止页码：164-176

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：;北大核心:【北大核心2023】；

基金：“十四五”国家重点研发计划项目(2023YFD2201500)。

语种：中文

中文关键词：刨花板表面缺陷;目标检测;多尺度;正偏态分布;YOLO

外文关键词：particleboard surface defect;object detection;multi-scale;positively skewed distribution;YOLO

分类号：S784;TP391.4

摘要：【目的】针对刨花板表面缺陷尺度变化大、多尺度缺陷共存及缺陷数量随缺陷尺度呈正偏态分布导致检测精度低的问题,提出一种具备自适应感受野能力的目标检测方法(PBDNet),用于提升刨花板表面缺陷检测的精度和速度。【方法】PBDNet通过引入空间分割与通道融合策略(SPDConv)作为下采样方法,将特征张量在空间上分割并在通道上拼接,减少下采样过程中的信息损失,为正偏态分布中高峰一侧的缺陷保留更多的细粒度特征,提高了检测模型对缺陷数量随缺陷尺度呈正偏态分布时的检测能力。此外,在PBDNet中提出的特征提取模块(C2f_SD),通过将可切换空洞卷积与差分卷积添加到C2f特征提取模块中,显著提高了模型对不同尺度缺陷的检测能力。【结果】对比试验及消融试验结果表明,PBDNet在mAP50值和Recall值上均优于主流缺陷检测算法。相较于YOLOv8s,PBDNet的mAP50值和Recall值分别提升4.8%和6.4%,达到0.881与0.840,同时在保持推理速度小于3 ms的情况下模型参数量减少42.2%。【结论】PBDNet检测方法能够满足刨花板表面多尺度缺陷正偏态分布时的检测需求,为实时精确检测的需求刨花板表面缺陷检测产业化应用提供了一种高效、精准、可边缘部署的自动化检测方法。
【Objective】To address the low detection accuracy caused by the large scale changes in particleboard surface defects,the coexistence of multi-scale defects,and the positively skewed distribution of defect quantities with respect to defect sizes,an object detection method with adaptive receptive field capability(PBDNet)was proposed in this study.This method was designed with an adaptive receptive field capability to improve the accuracy and efficiency in particleboard surface defect detection.【Method】By introducing the spatial splitting and channel fusion strategy(SPDConv)as the downsampling method,PBDNet spatially split feature tensors and concatenated them in channels,thereby reducing information loss during downsampling,and preserving more fine-grained features for defects on the high-frequency side of the positively skewed distribution.This method enhanced the detection ability of the detection model when the number of defects follows a positively skewed distribution with defect scale.Additionally,the feature extraction module(C2f_SD)proposed in PBDNet significantly improved the model's ability to detect defects of different scales by incorporating switchable atrous convolution and differential convolution into the C2f feature extraction module.【Result】The comparative and ablation experiments demonstrated that the PBDNet outperformed mainstream defect detection algorithms in terms of both mAP50 and Recall.Compared with YOLOv8s,PBDNet achieved improvements of 4.8%and 6.4%in mAP50 and Recall,reaching 0.881 and 0.840,respectively.Furthermore,the parameter count was reduced by 42.2%while nearly maintaining the inference speed under 3 ms.【Conclusion】The PBDNet detection method can meet the requirements for detection of the positively skewed distribution of multi-scale defects on particleboard surface.It provides an efficient,accurate,and edge-deployable automated solution for real-time precision detection,thereby facilitating industrial applications on particleboard surface defect detection.