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：Heng, Liu[1] Haomeng, Guo[1] Huize, Dai[1] Zheming, Chai[2,3] Chunyu, Li[4] Jianhua, Yang[1]

机构：[1] Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China; [2] School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150006, China; [3] Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; [4] Tangxian Huiyin Wood Industry Company Limited, Baoding, 072350, China

年份：2025

卷号：61

期号：12

起止页码：164-176

外文期刊名：Linye Kexue/Scientia Silvae Sinicae

收录：EI(收录号：20260620031008)

语种：中文

外文关键词：Convolution - Edge detection - Extraction - Particle board - Surface defects - Tensors

摘要：【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. ? 2025, Chinese Society of Forestry. All rights reserved.