木材科学与技术2025，Vol.39Issue(2)：57-66,81,11.DOI:10.12326/j.2096-9694.2024074
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
摘要
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.关键词
木材缺陷检测/YOLOv10/动态上采样/多尺度注意力机制Key words
wood defect detection/YOLOv10/dynamic upsampling/multi-scale attention mechanism分类
计算机与自动化引用本文复制引用
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(2):57-66,81,11.基金项目
赣州市科技计划项目"实木材料视觉分类与缺陷分析研究"(2022CXRC9269). （2022CXRC9269）
