基于改进YOLOv10s的木材表面缺陷检测模型

王隆平; 谢盛; 刘平

doi:10.12326/j.2096-9694.2024074

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基于改进YOLOv10s的木材表面缺陷检测模型
Wood Surface Defect Detection Model Based on Improved YOLOv10s
1.江西省赣州市南康区城发家具产业智能制造有限公司，江西赣州 341400
2.江西冠英智能科技股份有限公司，江西赣州 341000
2025年39卷第2期页码：57-66
DOI： 10.12326/j.2096-9694.2024074
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王隆平,谢盛,刘平.基于改进YOLOv10s的木材表面缺陷检测模型[J].木材科学与技术,2025,39(02):57-66. DOI： 10.12326/j.2096-9694.2024074.

WANG Longping,XIE Sheng,LIU Ping.Wood Surface Defect Detection Model Based on Improved YOLOv10s[J].Chinese Journal of Wood Science and Technology,2025,39(02):57-66. DOI： 10.12326/j.2096-9694.2024074.

摘要

针对木材表面缺陷复杂多样的问题及其检测任务的实时性需求，提升YOLOv10s模型性能，通过修改Backbone中卷积模块简化模型结构，同时引入轻量级动态上采样模块提高多尺度特征融合过程的采样精度，并采用多尺度注意力模块来兼顾大目标和小目标的检测。构建木材表面缺陷检测数据集对模型开展训练和验证试验，改进算法精度（mAP@0.5）达95.1%，推理时间为3.8 ms/帧。试验结果表明，改进模型在检测精度上优于同类算法，检测速度满足实时性需求，适应木材表面缺陷检测任务。

Abstract

In response to the complex and diverse nature of wood surface defects and the real-time requirements of the detection tasks

this study aimed to improve the performance of the YOLOv10s model for wood surface defect detection. The proposed method simplifies the model structure by modifying the convolution modules in the Backbone

while introducing a lightweight dynamic upsampling module to enhance the sampling accuracy in the multi-scale feature fusion process. Additionally

a multi-scale attention module is incorporated to balance the detection of both large and small targets. Furthermore

a wood surface defect detection dataset was constructed to train and validate the model. Experimental results show that the proposed improved algorithm achieves an mAP@0.5 (mAP@0.5 refers to the mean Average Precision at 50% intersection over union) of 95.1% and an inference time of 3.8 ms/frame. The results indicate that the improved model outperforms the current existing algorithms in detection accuracy

while it also meets the real-time detection requirements

making it suitable for wood surface defect detection tasks.

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references

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