面向刨花板表面多尺度缺陷正偏态分布的目标检测方法

doi:10.11707/j.1001-7488.LYKX20250130

Abstract

Abstract:

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.

Key words: particleboard surface defect, object detection, multi-scale, positively skewed distribution, YOLO

CLC Number:

S784
TP391.4

Heng Liu,Haomeng Guo,Huize Dai,Zheming Chai,Chunyu Li,Jianhua Yang. Object Detection Method for Positively Skewed Distribution of Multi-Scale Defects on Particleboard Surface[J]. Scientia Silvae Sinicae, 2025, 61(12): 164-176.

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模型名称 Model Name	mAP₅₀	mAP_50-95	召回率 Recall	参数量 Parameters (M)	推理时间 Time/ms
Faster R-CNN	0.702	0.445	0.541	84.70	25.10
YOLOv8s	0.833	0.619	0.776	11.14	2.49
RTMDet-s	0.836	0.566	0.676	39.00	54.20
RT-DETR	0.793	0.583	0.776	42.78	8.10
YOLOv10s	0.801	0.575	0.684	8.07	3.05
PBDNet	0.881	0.655	0.840	6.43	2.46

模型 Model	通道裁剪 Slim	SPDConv	C2f_SD	mAP₅₀	mAP_50-95	召回率 Recall	参数量 Parameters（M）	推理时间 Time/ms
A	—	—	—	0.833	0.619	0.776	11.14	2.49
B	—	√	—	0.828	0.61	0.747	10.11	2.51
C	—	—	√	0.849	0.653	0.818	14.42	4.98
D	—	√	√	0.85	0.636	0.791	13.60	5.12
E	√	—	—	0.845	0.634	0.785	8.48	2.39
F	√	√	—	0.857	0.623	0.823	5.50	1.65
G	√	—	√	0.867	0.648	0.804	6.94	2.34
H	√	√	√	0.881	0.655	0.840	6.40	2.46

缺陷类别 Defect class	mAP₅₀
缺陷类别 Defect class	A	B	C	D	E	F	G	H
斑状缺陷 Spot-like defect	0.814	0.832	0.815	0.831	0.822	0.825	0.850	0.863
刨花 Shavings	0.833	0.780	0.859	0.842	0.88	0.818	0.893	0.883
油污 Oil pollution	0.462	0.530	0.523	0.541	0.484	0.589	0.543	0.618
崩边 Edge breakage	0.973	0.958	0.971	0.943	0.971	0.969	0.972	0.958
粉笔记号 Chalk mark	0.902	0.919	0.913	0.934	0.911	0.966	0.951	0.968
划痕 Scratch	0.890	0.811	0.910	0.910	0.895	0.879	0.893	0.926
裂纹 Crack	0.954	0.968	0.953	0.950	0.954	0.953	0.964	0.953

缺陷类别 Defect class	召回率Recall
缺陷类别 Defect class	A	B	C	D	E	F	G	H
斑状缺陷 Spot-like defect	0.721	0.721	0.754	0.754	0.803	0.787	0.820	0.816
刨花 Shavings	0.764	0.739	0.857	0.810	0.832	0.810	0.810	0.857
油污 Oil pollution	0.402	0.325	0.500	0.400	0.359	0.566	0.428	0.575
崩边 Edge breakage	0.967	0.933	0.967	0.933	0.967	0.945	0.967	0.933
粉笔记号 Chalk mark	0.786	0.821	0.872	0.893	0.821	0.879	0.856	0.893
划痕 Scratch	0.845	0.743	0.829	0.800	0.771	0.829	0.800	0.861
裂纹 Crack	0.944	0.944	0.944	0.944	0.944	0.944	0.944	0.944

模型 Model	mAP₅₀	mAP_50-95	召回率 Recall	模型 Model	mAP₅₀	mAP_50-95	召回率 Recall
A	0.456	0.303	0.492	E	0.468	0.309	0.542
B	0.385	0.246	0.429	F	0.627	0.371	0.700
C	0.474	0.297	0.517	G	0.559	0.353	0.567
D	0.472	0.296	0.521	H	0.705	0.427	0.746

Object Detection Method for Positively Skewed Distribution of Multi-Scale Defects on Particleboard Surface

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